Department of Science and Technology of Shandong Province

The study first demonstrated that burdock fructooligosaccharide (BFO) could inhibit peel browning in green banana, with 0.5% BFO treatment showing the most significant suppression of peel browning during low-temperature storage (7 ± 1 °C). The results revealed that 0.5% BFO treatment effectively restrained the increase in electrolyte leakage and malondialdehyde (MDA) content and maintained cell membrane integrity. Furthermore, BFO treatment enhanced total phenolic content and antioxidant capacity, alleviated oxidative damage, and better preserved the external quality of banana peel. Simultaneously, BFO treatment markedly inhibited both the activities of chlorophyll-degrading enzymes and their relative gene expression levels in banana peel, thereby maintaining higher chlorophyll content. This research provided a new insight into the mechanism of inhibiting peel browning for low-temperature storage preservation of banana.

Traditional manual inspection approaches face challenges due to the reliance on the experience and alertness of operators, which limits their ability to meet the growing demands for efficiency and precision in modern manufacturing processes. Deep learning techniques, particularly in object detection, have shown significant promise for various applications. This paper proposes an improved YOLOv11-based method for surface defect detection in electronic products, aiming to address the limitations of existing YOLO models in handling complex backgrounds and small target defects. By introducing the MD-C2F module, DualConv module, and Inner_MPDIoU loss function, the improved YOLOv11 model has achieved significant improvements in precision, recall rate, detection speed, and other aspects. The improved YOLOv11 model demonstrates notable improvements in performance, with a precision increase from 90.9% to 93.1%, and a recall rate improvement from 77.0% to 84.6%. Furthermore, it shows a 4.6% rise in mAP50, from 84.0% to 88.6%. When compared to earlier YOLO versions such as YOLOv7, YOLOv8, and YOLOv9, the improved YOLOv11 achieves a significantly higher precision of 89.3% in resistor detection, surpassing YOLOv7’s 54.3% and YOLOv9’s 88.0%. In detecting defects like LED lights and capacitors, the improved YOLOv11 reaches mAP50 values of 77.8% and 85.3%, respectively, both outperforming the other models. Additionally, in the generalization tests conducted on the PKU-Market-PCB dataset, the model’s detection accuracy improved from 91.4% to 94.6%, recall from 82.2% to 91.2%, and mAP50 from 91.8% to 95.4%.These findings emphasize that the proposed YOLOv11 model successfully tackles the challenges of detecting small defects in complex backgrounds and across varying scales. It significantly enhances detection accuracy, recall, and generalization ability, offering a dependable automated solution for defect detection in electronic product manufacturing.

With the rapid development of the air transportation industry, the dynamic volatility of airline ticket prices has become increasingly prominent. Accurate price prediction is of great significance for airline revenue management and passenger ticket purchasing decisions. Based on real data from four major air routes in China, this paper proposes a flight price prediction method using a Random Forest (RF) model optimized by the Grey Wolf Optimizer (GWO) algorithm. First, the research status of airline ticket price prediction is systematically reviewed, and the advantages and disadvantages of traditional methods are analyzed. Then, to address the sensitivity of random forest model hyperparameters, the GWO algorithm is introduced for parameter optimization, which significantly improves the prediction accuracy and stability of the model. Experimental results show that the GWO-RF model outperforms mainstream methods in multiple evaluation metrics, especially demonstrating good generalization ability across different days of the week and various advance booking periods. Further predictions for the next two weeks indicate that this method can effectively capture price fluctuation patterns, providing scientific ticket purchasing advice for passengers and data support for airlines to optimize pricing strategies. This research not only enriches the theoretical system of flight price prediction but also offers new ideas for the application of intelligent optimization algorithms in complex regression problems.

Abstract This paper proposes an improved algorithm to address the efficiency issues of the RRT method in a 2D virtual environment in terms of spatiotemporal complexity. Firstly, an adaptive step size strategy is used to determine the appropriate global initial step size for a given map. Then, a target bias strategy is employed to assign a probability value to the sampling point as the target point, reducing the randomness of the sampling point. Then, incorporating the concept of target gravity improves the goal orientation of the algorithm, making the gravity coefficient adjustable. Finally, the pruning optimization strategy is used to remove redundant points in the initial path, and the pruned optimized path is smoothed. It has been verified that the proposed algorithm has advantages in planning time, path length, and number of path nodes, demonstrating the effectiveness of this method.

High salt in the soil limits plant growth and crop productivity. Under salt stress, plants have evolved various adaptive mechanisms, among which protein degradation by ubiquitination is an effective one. SGT1 is an inhibitory factor of the S-phase kinase-associated protein 1 (Skp1) G2 allele. It is well known that SGT1 is a key regulator of R-gene-mediated disease resistance. But little is known about its function and molecular mechanism in regulating salt stress tolerance. In this study, using yeast two-hybridization analysis, we identified an SGT1 in wheat. It was induced for expression by NaCl and exogenous abscisic acid (ABA) treatment. The fluorescence signal of TaSGT1-GFP fusion protein was located on the endoplasmic reticulum membrane. TaSGT1B overexpressing transgenic wheat exhibited more well-developed roots and earlier flowering but had a lower plant height compared to the wild-type plants. Also, overexpression of TaSGT1B increased salt, PEG, and drought stress tolerance as well as ABA sensitivity in wheat. While the TaSGT1 edited wheat lines showed opposite phenotypes. Interaction analysis demonstrated TaSGT1 directly interacted with the ubiquitin ligase TaRGLG2. Further, TaRGLG2 interacted with TaPP2Ca (5, 7, 8, 9) and could mediate their ubiquitination. The activity of TaPP2Ca was lower in TaSGT1B overexpressing transgenic wheat than in the wild-type line under salt stress. All these results demonstrated that TaSGT1 mediated the ubiquitin-mediated degradation of the negative regulatory factor TaPP2Ca through a TaSGT1-TaRGLG2-TaPP2Ca model, thereby enhancing salt stress tolerance.

In order to have a stronger bond with the surrounding bone, the bone prosthesis needs to have interconnecting pores for bone cells to grow and more importantly to avoid stress shielding. At the same time, human bones have different composition and structure of bone tissue in different parts of the body due to different physical factors of the person, so the elastic modulus of the bones that need to be supported and replaced are not the same. And additive manufacturing has the advantages of rapid, efficient and precise manufacturing of complex shapes and high-quality three-dimensional structures, which can manufacture porous scaffold bone prosthesis, and achieve more accurate mechanical property requirements by controlling the design parameters. To study the effect of design strut length and design strut cross-section diameter size on the elastic modulus of tetrahedral titanium alloy scaffold unit, and with the help of UG NX, several digital models of porous titanium alloy scaffolds were constructed with the strut length and the strut cross-section diameter size as the parameters of variation, and then the elastic modulus of each porous titanium alloy scaffold was measured by ANSYS Workbench 2022, and the elasticity modulus of each porous titanium alloy scaffold was further derived from the relationship between the strut length and strut cross-section diameter size and the porous titanium alloy scaffold. Then the elastic modulus of each porous titanium alloy bracket was measured by ANSYS Workbench 2022, and the mathematical model between the strut length, strut cross-section size and elastic modulus of the porous titanium alloy bracket was further derived. Then, ANSYS Workbench 2022 was used to measure the elastic modulus of each porous titanium alloy bracket and further derive the mathematical model between strut length, strut cross-section diameter size and elastic modulus of the porous titanium alloy bracket, with the help of which the elastic modulus of the porous titanium alloy bracket with specific diameters and strut lengths was finally deduced to validate the correctness of the above predicted mathematical model, and to make reasonable explanations and corrections for the deviations. explanation and correction of deviations. As a result, the rapid prototyping technology can be used to design the required porous titanium alloy bracket in a more detailed way.

This retrospective study aimed to evaluate the relationship between plasma autoantibody species and rhTPO response in adult ITP patients who failed the first-line treatments. Plasma anti-glycoprotein (GP) IIb/IIIa and anti-GPIb/IX autoantibodies were detected in 47·2% and 40·6% of the 123 patients, respectively. Overall response rate to rhTPO treatment in patients without anti-GPIb/IX autoantibodies was significantly higher than patients with anti-GPIb/IX autoantibodies (82·2% vs. 60·0%, P=0·006). By contrast, no statistical difference in response rate was observed between patients with or without anti-GPIIb/IIIa autoantibodies (74·1% vs. 72·3%, P=0·819). Therefore, the presence of anti-GPIb/IX autoantibodies might serve as a predictive factor for poor response to rhTPO treatment in ITP.