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The low-temperature impact toughness of nodular cast iron can be significantly enhanced by heat treatment, and thus meet the severe service requirements in the fields of high-speed rail and power generation, etc. In order to explore the enhancement mechanism, microstructure, hardness, composition and other characteristics of as-cast and heat-treated nodular cast iron is systematically tested and compared by optical microscopy, microhardness tester, EBSD, SEM, electron probe, and impact toughness testing machine in this study. The results show that heat treatment has little effect on the morphology and size of graphite in nodular cast iron, ignores the effect on the grain size, morphology, and distribution of ferritic matrix, and has little effect on the hardness and exchange of elements, while it is meaningful to find that heat treatment brings about significant decrease in high-angle grain boundaries (HAGB) between 59° and 60°, decreasing from 10% to 3%. Therefore, the significant enhancement of low-temperature impact toughness of nodular cast iron by heat treatment may result from the obvious decrease in HAGB between 59° and 60°, instead of other reasons. From this perspective, the study can provide novel ideas for optimizing the heat treatment process of nodular cast iron. [ABSTRACT FROM AUTHOR]

Site quality evaluation is an important foundation for decision-making and planning in forest management and provides scientific decision support and guidance for the sustainable development of forests and commercial plantations. Site index and site form models were constructed and subsequently compared utilizing fir (Cunninghamia lanceolata) plantations in Nanping City, Fujian Province, China. This papers aim was to construct a site quality classification model, conduct further analysis on the effects of different site factors on the quality of the site, and achieve an assessment of site quality for Chinese fir plantations. An algebraic difference approach was used to establish a site index model and a site form model for Chinese fir in Fujian Province. The suitability of the two models was compared using model accuracy analysis and partial correlation, and the optimal model was chosen for classifying the site quality of the stands. On this basis, a site quality classification model was established using the random forest algorithm, and the importance of each site factor was determined through importance ranking in terms of their impact on site quality. Within the study area, the R2 of the site index model results was 0.581, and the R2 values of the five site form models based on different reference breast diameters, ranked from high to low, were 0.894, 0.886, 0.884, 0.880, and 0.865. The bias correlation coefficient between site form and stand volume was 0.71, and the bias correlation coefficient between site index and stand volume was 0.52. The results confirmed that the site form model is better suited for evaluating the site quality of Chinese fir plantations. The random forest-based site form classification model had a high classification accuracy with a generalization accuracy of 0.87. The factors that had the greatest impact on site form were altitude, canopy closure, and slope gradient, whereas landform had the smallest impact on site form. These results can provide a reference for the evaluation of the site quality of plantations and natural forests in southern China to ensure the long-term sustainable use of forest resources. [ABSTRACT FROM AUTHOR]

Bone age is the chronological age of human bones, which serves as a key indicator of the maturity of bone development and can more objectively reflect the extent of human growth and development. The prevalent viewpoint and research development direction now favor the employment of deep learning-based bone age detection algorithms to determine bone age. Although bone age detection accuracy has increased when compared to more established methods, more work needs to be conducted to raise it because bone age detection is primarily used in clinical medicine, forensic identification, and other critical and rigorous fields. Due to the symmetry of human hand bones, bone age detection can be performed on either the left hand or the right hand, and the results are the same. In other words, the bone age detection results of both hands are universal. In this regard, the left hand is chosen as the target of bone age detection in this paper. To accomplish this, the You Only Look Once-v5 (YOLOv5) and Residual Network-34 (ResNet34) integration techniques are combined in this paper to create an innovative bone age detection model (YARN), which is then combined with the RUS-CHN scoring method that applies to Chinese adolescent children to comprehensively assess bone age at multiple levels. In this study, the images in the hand bone dataset are first preprocessed with number enhancement, then YOLOv5 is used to train the hand bone dataset to identify and filter out the main 13 joints in the hand bone, and finally, ResNet34 is used to complete the classification of local joints and achieve the determination of the developmental level of the detected region, followed by the calculation of the bone age by combining with the RUS-CHN method. The bone age detection model based on YOLOv5 and ResNet34 can significantly improve the accuracy and efficiency of bone age detection, and the model has significant advantages in the deep feature extraction of key regions of hand bone joints, which can efficiently complete the task of bone age detection. This was discovered through experiments on the public dataset of Flying Paddle AI Studio. [ABSTRACT FROM AUTHOR]

Sudden changes in the morphological characteristics of trees are closely related to plant health, and automated phenotypic measurements can help improve the efficiency of plant health monitoring, and thus aid in the conservation of old and valuable tress. The irregular distribution of branches and the influence of the natural environment make it very difficult to monitor the status of branches in the field. In order to solve the problem of branch phenotype monitoring of tall and valuable plants in the field environment, this paper proposes an improved UNet model to achieve accurate extraction of trunk and branches. This paper also proposes an algorithm that can measure the branch length and inclination angle by using the main trunk and branches separated in the previous stage, finding the skeleton line of a single branch via digital image morphological processing and the Zhang–Suen thinning algorithm, obtaining the number of pixel points as the branch length, and then using Euclidean distance to fit a straight line to calculate the inclination angle of each branch. These were carried out in order to monitor the change in branch length and inclination angle and to determine whether plant branch breakage or external stress events had occurred. We evaluated the method on video images of Abies beshanzuensis, and the experimental results showed that the present algorithm has more excellent performance at 94.30% MIoU as compared with other target segmentation algorithms. The coefficient of determination (R2) is higher than 0.89 for the calculation of the branch length and inclination angle. In summary, the algorithm proposed in this paper can effectively segment the branches of tall plants and measure their length and inclination angle in a field environment, thus providing an effective method to monitor the health of valuable plants. [ABSTRACT FROM AUTHOR]

MXene materials have found emerging applications as catalysts for chemical reactions due to their intriguing physical and chemical applications. In particular, their broad light response and strong photothermal conversion capabilities are likely to render MXenes promising candidates for photothermal catalysis, which is drawing increasing attention in both academic research and industrial applications. MXenes are likely to satisfy all three criteria of a desirable photothermal catalyst: strong light absorption, effective heat management, and versatile surface reactivity. However, their specific functionalities are largely dependent on their structure and composition, which makes understandings of the structure–function relationship of crucial significance. In this review, we mainly focus on the recent progress of MXene–based photothermal catalysts, emphasizing the functionalities and potential applications of MXene materials in fields of photothermal catalysis, and provide insights on design principles of highly efficient MXene–based photothermal catalysts from the atomic scale. This review provides a relatively thorough understanding of MXene–based materials for photothermal catalysis, as well as an in–depth investigation of emerging high-prospect applications in photothermal catalysis. [ABSTRACT FROM AUTHOR]

The strain of fiber-reinforced polymer (FRP) bars at high temperatures is currently difficult to measure. To overcome this difficulty, a method of smart FRP bars embedded with optical fibers was proposed and studied, in which an ordinary single-mode optical fiber was applied as a distributed sensor. In this paper, both the distributed temperature and strain-sensing characteristics of optical fiber were studied based on pulse pre-pump Brillouin optical time-domain analysis (PPP-BOTDA) under high temperature. The temperature and strain coefficients were investigated under a thermomechanical coupling environment with consideration of large strain levels. The experimental results show that the temperature and strain coefficients decreased as the temperature increased, because the properties of silica and coating materials changed with temperature. Then, the formulas for determining the temperature and strain coefficients at high temperatures were introduced and discussed. The excellent sensing performance of the optical fiber indicated that smart FRP bars have the potential for use at high temperatures. [ABSTRACT FROM AUTHOR]