An appropriate drying method is crucial for producing high-quality dried apricots. In this study, the effects of four drying methods, hot air drying (HAD), infrared drying (IRD), pulse vacuum drying (PVD), and vacuum freeze-drying (VFD), on the drying kinetics and physical and nutritional characteristics of apricot slices were evaluated. PVD required the shortest time (16.25 h), followed by IRD (17.54 h), HAD (21.39 h), and VFD (34.64 h). VFD resulted in the best quality of apricot slices, with the smallest color difference (ΔE = 13.64), lowest water activity (0.312 ± 0.015) and browning degree (0.35), highest color saturation (62.84), lowest hardness (8.35 ± 0.47 N) and shrinkage (9.13 ± 0.65%), strongest rehydration ability (3.58 ± 0.11 g/g), a good microstructure, and high nutrient-retention rates (ascorbic acid content: 53.31 ± 0.58 mg/100 g, total phenolic content: 12.64 ± 0.50 mg GAE/g, and carotenoid content: 24.23 ± 0.58 mg/100 g) and antioxidant activity (DPPH: 21.10 ± 0.99 mmol Trolox/g and FRAP: 34.10 ± 0.81 mmol Trolox/g). The quality of PVD-treated apricot slices was second-best, and the quality of HAD-treated apricot slices was the worst. However, the energy consumption required for VFD was relatively high, while that required for PVD was lower. The results of this study provide a scientific basis for the large-scale industrial production of dried apricots.

Drying is one of the best methods to preserve the quality of fresh fruits and prolong their shelf life. This study focuses on Prunus armeniaca L. cv. ‘Diaogan’ (commonly known as Diaogan apricot) sourced from Xinjiang, China to explore the impact of two natural drying methods (shade drying and open-air drying in the rocky desert) on the drying kinetics, color, textural characteristics, microstructure, chemical properties, and antioxidant capacity of Diaogan apricots. The experimental results indicate that throughout the natural drying process, the time required for open-air drying in the rocky desert was reduced by 26.47% compared to shade drying. The L*, a*, and b* values of the shade- and ventilation-dried Diaogan apricots were higher than those sun-dried in the rocky desert, exhibiting a lower color difference (ΔE) than apricots dried through rocky desert sun drying. Specifically, the ΔE for shade-dried Diaogan apricots was 19.66 ± 0.24. The Diaogan apricots dried in the rocky desert exhibited greater hardness, lower elasticity, stronger adhesiveness, and higher chewiness compared to those dried in the shade, with the hardness, adhesiveness, and chewiness being, respectively, 14.71%, 18.89%, and 35.79% higher. Scanning electron microscopy (SEM) observations revealed that the high temperatures experienced during open-air drying in the rocky desert caused rapid dehydration of the Diaogan apricot’s skin, leading to clogging and crust formation in the flesh pores, along with deformation or tearing of the tissue structure, ultimately resulting in poor rehydration ability. After drying, there was a significant increase in the soluble solids in the Diaogan apricots, whereas titratable acidity, total phenols, ascorbic acid, and antioxidant capacity were significantly decreased (p < 0.05). In summary, the quality of dried Diaogan apricots post-drying is dependent on the natural drying method employed, with shade drying resulting in superior quality of Diaogan apricots compared to open-air drying in the rocky desert. This study offers fundamental data and serves as a theoretical reference for the industrialized production of apricots.

This study focuses on the problem of corn yield prediction, and a novel prediction model based on a dynamic ensemble stacking regression algorithm is proposed. The model aims to achieve more accurate corn yield prediction based on the in-depth exploration of the potential correlations in multisource and multidimensional data. Data on the weather conditions, mechanization degree, and maize yield in Qiqihar City, Heilongjiang Province, from 1995 to 2022, are used. Important features are determined and extracted effectively by using principal component analysis and indicator contribution assessment methods. Based on the combination of an early stopping mechanism and parameter grid search optimization, the performance of eight base models, including a deep learning model, is fine-tuned. Based on the theory of heterogeneous ensemble learning, a threshold is established to stack the high-performing models, realizing a dynamic ensemble mechanism and employing averaging and optimized weighting methods for prediction. The results demonstrate that the prediction accuracy of the proposed dynamic ensemble regression model is significantly better as compared to the individual base models, with the mean squared error (MSE) being as low as 0.006, the root mean squared error (RMSE) being 0.077, the mean absolute error (MAE) being 0.061, and a high coefficient of determination value of 0.88. These findings not only validate the effectiveness of the proposed approach in the field of corn yield prediction but also highlight the positive role of multisource data fusion in enhancing the performance of prediction models.

To address the issues of manual inspection and low precision in the detection and recognition of defects in existing animal leather, this study first establishes a leather image dataset and applies an improved Gabor filtering algorithm for image preprocessing. Specifically, the weighted average method is adopted to grayscale the image, and the algorithm parameters are designed and improved to ensure that most of the key texture information of the leather images is obtained, meeting the requirements for texture feature information in subsequent feature extraction. Next, it explores statistical feature extraction algorithms based on the gray-level co-occurrence matrix and the statistical feature extraction algorithm based on gray-level distribution, forming a combination of features for the dataset. The leather defects mainly include warble fly holes, neck wrinkles, and scars. In the processing process, there are also defects such as scratches, holes, and stains. Finally, a leather defect image classification model is proposed based on a multilayer perceptron algorithm, using the ReLU activation function and a SoftMax classifier to classify surface defects in 1280 samples. The classification time is 0.0854 s, and the average precision, recall, and accuracy for leather defect image classification are all 99.53%. This solution innovatively integrates the improved Gabor filtering with the adaptive multilayer perceptron architecture to construct a multi-modal leather defect classification model, which significantly improves the detection accuracy of three types of defects, namely holes, scratches, and stains. It provides a theoretical reference for the automation of the leather processing process.

With increased heat control requirements for high-heat-flux products in a narrow heat dissipation space, the ultra-thin micro-heat pipe (MHP) with high heat transfer performance has become an ideal heat dissipation component. In this study, the computational fluid dynamics (CFD) method is used to conduct three-dimensional modeling based on the geometric structure characteristics of an ultra-thin MHP. The capillary pressure of the sintered wick is represented by the modified parameter, and a simple and valuable heat and mass transfer model of the ultra-thin MHP is established by fitting the real experimental data through parameter modification. The flow situation of the working medium inside the ultra-thin MHP is analyzed based on the abovementioned parameters. The results show that when the modified parameter is α = 1.5, the temperature equalization requirements of the ultra-thin MHP can be met to the best degree. Moreover, with an increase in heating power, the error value between the surface temperature data of the model and the experimental data of the ultra-thin MHP sample decreases. Under different heating powers, the working medium inside the ultra-thin MHP has the same flow trend. In addition, a 40% increase in temperature difference is found at the junction of the heating section and the adiabatic section, leading to a fluctuation in the temperature gradient on the heat pipe surface. The research results provide a theoretical basis for the model establishment, heat and mass transfer performance investigation, and parameter optimization of ultra-thin MHPs.

With the advancement of intelligent technology and the rise in labor costs, manual identification and cutting of 3D cotton cup indentations can no longer meet modern demands. The increasing variety and shape of 3D cotton cups due to personalized requirements make the use of fixed molds for cutting inefficient, leading to a large number of molds and high costs. Therefore, this paper proposes a UNet-based indentation segmentation algorithm to automatically extract 3D cotton cup indentation data. By incorporating the VGG16 network and Leaky-ReLU activation function into the UNet model, the method improves the model’s generalization capability, convergence speed, detection speed, and reduces the risk of overfitting. Additionally, attention mechanisms and an Atrous Spatial Pyramid Pooling (ASPP) module are introduced to enhance feature extraction, improving the network’s spatial feature extraction ability. Experiments conducted on a self-made 3D cotton cup dataset demonstrate a precision of 99.53%, a recall of 99.69%, a mIoU of 99.18%, and an mPA of 99.73%, meeting practical application requirements. The extracted 3D cotton cup indentation contour data is automatically input into an intelligent CNC cutting machine to cut 3D cotton cup. The cutting results of 400 data points show an 0.20 mm ± 0.42 mm error, meeting the cutting accuracy requirements for flexible material 3D cotton cups. This study may serve as a reference for machine vision, image segmentation, improvements to deep learning architectures, and automated cutting machinery for flexible materials such as fabrics.