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12-mm-thick Al–Li–Cu alloy plates were fabricated by stationary shoulder friction stir welding (SSFSW) to evaluate the softening behavior of the whole joint under a given rotational speed of 400 rpm and a welding speed of 60 mm/min. Continuous dynamic recrystallization, discontinuous dynamic recrystallization, and geometric dynamic recrystallization are the main dynamic recrystallization mechanisms in SSFSWed joints. Such mechanisms help to gradually change rolling grains of base material (BM) into equiaxed grains of welding nugget zone (WNZ) and significantly lower dislocation density. The decrease in dislocation density is the main reason for joint softening. Continuous coarsening of T1/θ′ in heat affected zone (HAZ) and thermo-mechanically affected zone (TMAZ) weakens the strengthening effect. The development of precipitate-free zones and re-precipitation of TB primarily contribute to severe softening at the interface between HAZ and TMAZ on the advancing side (AS-HAZ-TMAZ). The serious softening behavior at the bottom of WNZ is attributed to the re-precipitation of TB and the complete dissolution of T1/θ′. Hardness profiles and tensile strength distribution of SSFSWed joint exhibit a “W” pattern, while a “U” pattern appears at the bottom. The hardness profile and tensile strength of WNZ are significantly poorer at 8–12 mm from the weld surface compared to those at 0–8 mm. Softening behavior also results from the higher fractions of the maximum Schmid factor (in 0.4–0.5) and Cube. From BM to WNZ, fracture mode transitions from quasi-cleavage fracture to plastic fracture.

Linguistic knowledge helps a lot in scene text recognition by providing semantic information to refine the character sequence. The visual model only focuses on the visual texture of characters without actively learning linguistic information, which leads to poor model recognition rates in some noisy (distorted and blurry, etc.) images. In order to address the aforementioned issues, this study builds upon the most recent findings of the Vision Transformer, and our approach (called Display-Semantic Transformer, or DST for short) constructs a masked language model and a semantic visual interaction module. The model can mine deep semantic information from images to assist scene text recognition and improve the robustness of the model. The semantic visual interaction module can better realize the interaction between semantic information and visual features. In this way, the visual features can be enhanced by the semantic information so that the model can achieve a better recognition effect. The experimental results show that our model improves the average recognition accuracy on six benchmark test sets by nearly 2% compared to the baseline. Our model retains the benefits of having a small number of parameters and allows for fast inference speed. Additionally, it attains a more optimal balance between accuracy and speed.

The global network threat is becoming more and more serious, and network security vulnerability management has become one of the critical areas in the national information security emergency system construction. To guide the third-party sharing platforms regarding network security vulnerability management, this work constructs a signal game model comprising third-party vulnerability sharing platforms and software vendors for vulnerability collaborative disclosures. In addition, we analyze the game strategy selection and its influencing factors. The results show that there are two perfect Bayesian equilibria, including separation equilibrium and mixed equilibrium, due to the incomplete lines of information disclosure. The equilibrium state is mainly based on the compression time of the protection period and the existence ratio of the software vendors who develop the patches in the market. This work puts forward some suggestions in terms of the protection period, reputation loss, and relevant laws and regulations.

In fine-grained visual classification(FGVC), small inter-class variations and large intra-class variations are always inherent attributes, so it is much more challenging than traditional classification tasks. Recent studies have mainly tackled this problem by employing attention mechanisms to locate the most discriminative parts. However, these methods tend to neglect other inconspicuous but distinguishable parts, and can not effectively fuse the features information of different scales and different degrees of discrimination. In this paper, we propose a multi-scale progressive complementary fusion network (MPCF-Net) to address these problems. In particular, we propose the following: (i) A three-step multi-scale progressive training method, which employs an image slicer to generate puzzle images at different scales followed by multi-step progressive training. This enables the network to capture multi-granularity local feature information and gradually expand its attention to global structural information as the training progresses for multi-granularity information fusion. (ii) A plug-and-play feature complementary enhancement module (FCEM) that explicitly enhances the features extracted by the current layer of the network, while also enabling the next layer of the network to extract potential complementary feature information to diversify the features. Our experiments were conducted on four FGVC benchmark datasets and yielded state-of-the-art and competitive results.

Patients undergoing immunotherapy always exhibit a low-response rate due to tumor heterogeneity and immune surveillance in the tumor. Angiogenesis plays an important role in affecting the status of tumor-infiltrated lymphocytes by inducing hypoxia and acidosis microenvironment, suggesting its synergistic potential in immunotherapy. However, the antitumor efficacy of singular anti-angiogenesis therapy often suffers from failure in the clinic due to the compensatory pro-angiogenesis signaling pathway. In this work, classic injectable thermosensitive PLGA-PEG-PLGA copolymer was used to construct a platform to co-deliver CA4P (vascular disruptive agent) and EPI for inducing immunogenic cell death of cancer cells by targeting the tumor immune microenvironment. Investigation of 4T1 tumor-bearing mouse models suggests that local administration of injectable V+E@Gel could significantly inhibit the proliferation of cancer cells and prolong the survival rate of 4T1 tumor-bearing mouse models. Histological analysis further indicates that V+E@Gel could effectively inhibit tumor angiogenesis and metastasis by down-regulating the expression of CD34, CD31, MTA1 and TGF-β. Moreover, due to the sustained release kinetics of V+E@Gel, its local administration relieves the immune surveillance in tumor tissues and thus induces a robust and long-lasting specific antitumor immune response. Overall, this work provides a new treatment strategy through the mediation of the tumor immune microenvironment by vascular disruption to fulfill enhanced chemotherapy and immunotherapy.

In this study, Friction plug welding (FPW) for 8 mm thickness AA2219-T87 sheets were carried out, and defect-free joints were obtained. The geometric size of plug and plate hole, rotational speed and welding force exhibit significant effects on the weld formation. Meanwhile, it is concluded that significant inhomogeneity of microstructure and mechanical properties exists in FPW joints. The recrystallization zone has the highest mechanical properties owing to the fine equiaxed grains and uniformly distributed θ precipitates. The entire plug, thermo-mechanically affected zone and nugget thermo-mechanically affected zone closed to the bonding interface are significantly softened due to the deformation of the grains and θ’ precipitate dissolution. The ultimate tensile strength (UTS) and elongation of the FPW joints can reach 359 MPa and 7.3% at 77 K and 305 MPa and 5% at 298 K, respectively.

Girth weld cracking of long-distance oil and gas pipelines yields substantial harm to pipeline safety and may cause serious accidents. As of today, non-destructive testing has been one of the most common methods for predicting potential faults and ensuring safe operation. Classical pipeline non-destructive testing methods include magnetic flux leakage testing and the use of ultrasonic testing by electromagnetic acoustic transducers. However, they are incapable of identifying the defects in complex surfaces like girth welds. Magnetic flux leakage testing exhibits poor anti-interference abilities and low space resolution. Ultrasonic testing by electromagnetic acoustic transducers suffer from low conversion efficiency and poor signal quality. In order to overcome the disadvantages of conventional pipeline non-destructive testing methods, we propose an embedded eddy current testing system by leveraging image processing and neural networks. Hough transform and the contour extraction technique are employed to extract the characteristic features from the two-dimensional (2D) eddy current impedance image. Experiment results show that the system can effectively identify the girth weld defects, featuring an accuracy of up to 92%. The low power consumption and compactness of the proposed system makes it a great candidate for pipeline inner inspection.