To address the issues of rapid surface wear and poor frictional performance of magnesium alloys, this study employed femtosecond laser processing to fabricate regular hexagonal micro-textures on the surface of AZ31B magnesium alloy, aiming to enhance its tribological properties. Friction and wear tests were conducted under two conditions—dry sliding and oil lubrication—to systematically evaluate the influence of surface texturing on friction coefficients and wear behavior. The results demonstrate that laser-induced texturing significantly reduces both the coefficient of friction and the wear extent. Under dry sliding conditions, the textured surface reduced the friction coefficient by more than 40
To investigate the enhancement effect of ultrasound on moisture transfer during far-infrared radiation combined vacuum drying, channel catfish surimi was used as model material, and ultrasound-assisted drying experiments coupled with two-dimensional correlation spectroscopy analysis were conducted. The effects of different ultrasonic powers on drying characteristics, water migration and molecular vibrations were evaluated. The results showed that the application of 30 W and 60 W ultrasound powers could shorten drying times by 10
Conventional porosity inversion methods typically derive elastic parameters from pre-stack seismic inversion and then convert them to porosity through rock physics relationships. Due to inherent issues of such stepwise strategies—such as error accumulation, strong model dependence, and insufficient utilization of the correlational information in logging—this paper proposes a novel porosity inversion method based on joint dictionary learning and spatial structure constraints from post-stack seismic data. This method abandons the traditional stepwise inversion strategy and innovatively constructs a joint sparse representation framework. First, leveraging logging data from the target area, it employs joint dictionary learning technology to extract the features of acoustic impedance and porosity as well as the complex nonlinear mapping relationship between them, thereby forming a joint dictionary containing their intrinsic correlation as prior knowledge. Subsequently, this dictionary is incorporated into the post-stack seismic inversion process, enabling the simultaneous inversion of impedance and porosity, which fundamentally avoids the error propagation inherent in stepwise inversion. Furthermore, to improve the spatial continuity of the inversion results, the Gradient Structure Tensor (GST) technique is introduced to extract the spatial structural features of the subsurface media from seismic data as a constraint, effectively enhancing the horizontal continuity and geological plausibility of the porosity model. Experiments on the Marmousi theoretical model demonstrate that compared to traditional linear regression methods, the proposed method reduces the root mean square error (RMSE) of the inversion results by approximately 30
Fusing visible (RGB) and thermal (T) images for RGBT tracking has received growing interest in the field of computer vision. However, how to improve the robustness of the tracker to target scale variety, effectively apply visual prompts to multimodal tracking tasks, and enhance the multimodal fusion effectiveness are still urgent challenges in the field of RGBT tracking. To this purpose, this work proposes an RGBT tracking framework integrating scale-aware dilation attention, multimodal prompt interaction learning, and cross- fusion adapter, named MPANet. Firstly, a scale-aware dilation attention (SADA) module is put forward to enhance the flexibility of the tracker in the presence of target scale variations by embedding convolutions with different dilation rates into the self-attention. Subsequently, a multimodal prompt interaction learning (MPIL) module is constructed, which combines global token adaptive attention and spatial attention to efficiently learn visual prompts from different modalities and achieve intermodal prompt interactions. Finally, a cross-fusion adapter (CFA) is developed to facilitate the adaptability of the network to different modalities in the process of multimodal information fusion through the adapter mechanism. Extensive experiments on public RGBT benchmark tracking datasets such as GTOT, RGBT234, LasHeR and VTUAV demonstrate that the proposed method outperforms existing advanced trackers and achieves state-of-the-art performance.
Hypochlorite ion (ClO-) and adenosine-5'-triphosphate (ATP) are crucial in various physiological functions. The connection between ClO-and ATP has been noted as one of the major characteristics of liver injury. This study presents a zeolitic imidazolate framework (ZIF-90)-based probe RhB@MP-ZIF-90 for the simultaneous identification of ClO-and ATP, which was fabricated by in situ encapsulation of rhodamine B (RhB) and successive modification using 4-methyl-1-(4-(4,4,5,5-tetramethyl-1,3,2-dioxa-borolan-2-yl)benzyl)pyridinium bromide (MP). A well-dispersed aqueous solution of this nano-scale probe is highly emissive in the green channel due to the charge transfer (CT) from ZIF-90 to the pyridinium. ClO-oxidizes the boronate ester group to a hydroxyl in the probe RhB@MP-ZIF-90, which is accompanied by the elimination of 4-methylene-cyclohexa-2,5-dienone, changing pyridinium to pyridine, blocking ICT, and quenching the green emission. By contrast, the ZIF-90 backbone of the probe is destroyed by ATP, releasing RhB to generate a characteristic red emission. Thus, this easy-to-prepare probe enabled the dual detection of ClO-and ATP, achieved through ClO--triggered fluorescence decrease and ATP-induced fluorescence amplification in two independent channels, with detection limits of 24.94 mu M for ClO-and 360 mu M for ATP, respectively. As such, RhB@MP-ZIF-90 was successfully used to monitor the concentration changes of mitochondrial ClO-and ATP in different biological processes and a drug-induced liver injury model.
