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Although acute promyelocytic leukaemia (APL) is a highly curable disease, challenges of early death (ED) and relapse still exist, and real-world data are scarce in the ATRA plus ATO era. A total of 1105 APL patients from 1990 to 2020 were enrolled and categorized into three treatment periods, namely ATRA, ATRA plus ATO, and risk-adapted therapy. The early death (ED) rate was 20.2%, 10.1%, and 7.0%, respectively, in three periods, while there was no significant decline in the 7-day death rate. Consistently, the overall survival (OS) and disease-free survival (DFS) of APL patients markedly improved over time. Despite the last two periods exhibiting similar DFS, the chemotherapy load was substantially lower in Period 3. Notably, leveraging older age and higher WBC count (especially > 50 × 109/L), we could identify a small group of extremely high-risk patients who had a very high ED rate and poor prognosis, while those with NRAS mutations and higher WBC tended to relapse, both representing obstacles to curing all patients. In conclusion, the evolvement of treatment paradigms can reduce the ED rate, improve clinical outcomes, and spare patients the toxicity of chemotherapy. Special care and innovative agents are warranted for the particularly high-risk APL.

Automatic weeding is a hot research field of smart agriculture, which has many benefits such as achieving precise weed control, saving human cost, and avoiding damage on crops, etc. Recently, many researchers have focused on the research using the deep learning method, such as the convolutional neural network (CNN) and recurrent neural network (RNN) and have achieved decent outcomes related to the automatic weed detection. However, there are still generally problems of the projects such as weak robustness and excessive reliance on a large number of samples. To solve these problems, a recognition algorithm for automatic identification and weed removal was designed, and a soybean field weed detection and localization method based on the fusion of multiple image processing methods was proposed in this study. The images and video stream were obtained through the camera mounted on a mobile robot platform. Firstly, the soil background inside the image was segmented from the foreground (including the weeds and crops) by setting the threshold for a specific color space (hue). Then, three different methods including the area threshold method, template matching and saturation threshold method were used to classify the crops and weeds. Finally, based on a proposed innovative voting method, the three recognition methods were comprehensively weighed and fused to achieve more accurate recognition and localization results of the crops and weeds inside the image. Experimental validations were carried out using the samples obtained through the moving platform, and the experimental results showed that the average accuracy of the proposed weed detection algorithm was as high as 98.21%, while the recognition error was only 1.79%. Meanwhile, compared with each single method as the scale threshold, template matching and saturation threshold, the fused method based on the weighted voting has been able to raise the average accuracy by 5.71%. Even though the samples used in the validations were limited in covering different scenarios, the high recognition accuracy has proved the practicability of the proposed method. In addition, the robustness test that images with raindrop and shadow interference in the complex and unstructured agricultural scene was carried out, and satisfied results showed that above 90% of the plant were successfully detected, which verified the fine adaptability and robustness of the proposed method.

7075 aluminum alloy has been widely used because of its excellent mechanical properties. The fatigue crack acceleration induced by compressive overloading is detrimental to 7075 aluminum alloy in service, and the corresponding mechanisms are complex. To further reveal these mechanisms, the effect of microstructure on fatigue damage response ahead of the crack tip is simulated based on the damage-coupled crystal plasticity model for 7075 aluminum alloy. Results show that the ratchetting strain ahead of the crack tip at the compressive overload ratio of −3 is greater than that at the compressive overload ratio of −2, which is attributed to the fact that higher compressive overloading induces greater tensile residual stress ahead of the crack tip. The proportion of material points with no less than four activated slip systems increases after compressive overloading, indicating the number of activated slip systems ahead of the crack tip increases because of the compressive overload effect. This causes the dislocation multiplication rate to be faster, thereby accelerating the fatigue damage accumulation after compressive overloading. Furthermore, the effect of compressive overload changes the slip system activity, and this change in slip system activity depends on grain orientation. It is also found that for a grain ahead of the crack tip, the acceleration of fatigue damage accumulation caused by the effect of compressive overload is influenced by the orientation of its nearest neighbor grains. The findings provide further insight into the acceleration mechanisms induced by the compressive overload effect from a microscopic perspective. [ABSTRACT FROM AUTHOR]

In the serviced components of a 7075 aluminum alloy, the propagation of fatigue crack can be retarded because of the overload effect; however, the corresponding retardation mechanisms are complex. To provide further insights into the retardation mechanisms of 7075 aluminum alloys, this study addresses the crack tip damage response of a cracked 7075 aluminum alloy under an overload effect. Based on the dual-scale modeling approach and the damage-coupled crystal plasticity model, the effect of the microstructure of a 7075 aluminum alloy on the damage behavior ahead of the crack tip under an overload was studied. The factors affecting fatigue damage accumulation ahead of the crack tip, such as dislocation density, the variation in the activities of slip systems, and the orientation effect of the nearest neighbor grains, are described. The results show that for the 7075 aluminum alloy, the compressive residual stress induced by the overload effect not only decreases the number of activated slip systems, but also lowers the rate of increase in dislocation density. This causes a decrease in fatigue damage accumulation during deformation. Moreover, the overload effect decreases the slip system activity as well as the resultant plastic slip; however, the decrease in plastic slip varies with the grain orientation, indicating that the overload effect depends on the grain orientation. It can also be found that both the damage strain energy release rate and lattice strain are influenced by the orientation of the nearest neighbor grains, which can eventually affect the overload effect. These findings contribute to understanding the retardation mechanisms from a microscopic perspective and provide guidance on improving the material design of a 7075 aluminum alloy to some extent. [ABSTRACT FROM AUTHOR]

Realizing high efficiency of all Si-based light-emitting devices is currently one of interesting issues in order to develop monolithic opto-electronic integration on chips. Here, we report an electroluminescence device based on phosphorus (P)-doped silicon nanocrystals (Si NCs)/silicon carbide (SiC) multilayers by modulating carrier injection and recombination process. The p+-Si substrate is used instead of p-Si substrate for facilitating the hole injection into Si NCs. Additionally, the influences of annealing temperature on the device performance have been studied, and the optimized annealing temperature is achieved by balancing the crystallinity, defect state density, and recombination process. [ABSTRACT FROM AUTHOR]

This paper reviewed the research progress in high-temperature-resistant modification of polycarbonate at home and abroad in recent years from two aspects: chemical and physical modifications. [ABSTRACT FROM AUTHOR]

A regular calligraphy script of each calligrapher has unique strokes, and a script's authenticity can be identified by comparing them. Hence, this study introduces a method for identifying the authenticity of regular script calligraphy works based on the improved YOLOv7 algorithm. The proposed method evaluates the authenticity of calligraphy works by detecting and comparing the number of single-character features in each regular script calligraphy work. Specifically, first, we collected regular script calligraphy works from a well-known domestic calligrapher and divided each work into a single-character dataset. Then, we introduced the PConv module in FasterNet, the DyHead dynamic detection header network, and the MPDiou bounding box loss function to optimize the accuracy of the YOLOv7 algorithm. Thus, we constructed an improved algorithm named YOLOv7-PDM, which is used for regular script calligraphy identification. The proposed YOLOv7-PDM model was trained and tested using a prepared regular script single-character dataset. Through experimental results, we confirmed the practicality and feasibility of the proposed method and demonstrated that the YOLOv7-PDM algorithm model achieves 94.19% accuracy (mAP50) in detecting regular script font features, with a single-image detection time of 3.1 m and 31.67M parameters. The improved YOLOv7 algorithm model offers greater advantages in detection speed, accuracy, and model complexity compared to current mainstream detection algorithms. This demonstrates that the developed approach effectively extracts stroke features of regular script calligraphy and provides guidance for future studies on authenticity identification. [ABSTRACT FROM AUTHOR]