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Reducing fertilizer pollution is an important direction for modern ecological agriculture. Commonly, excessive nitrogen is applied to pepper. Capsaicin is one of the most important economic qualities of spicy peppers, but the effect of nitrogen on capsaicin is still inconsistent. This study aims to elucidate the impact of nitrogen fertilizer on capsaicin accumulation and to provide guidance on fertilizer application on highly spicy chili peppers. The experiment was conducted with five nitrogen fertilizer concentrations: N1 (urea 750 kg ha−1), N2 (urea 562.5 kg ha−1), N3 (urea 375 kg ha−1), N4 (urea 187.5 kg ha−1), and N0 (no nitrogen fertilizer). Nitrogen treatment was applied to two varieties with different spiciness levels from the seedling stage. The fruits in different layers of pepper plants were sampled on the 20th, 35th, and 50th day after anthesis, and the fruits’ size, content of capsaicin, capsaicin precursors, capsaicin competitors, as well as capsaicin-related enzyme activities and gene expression level were analyzed. The results indicate that, when applying N2 and N3, both chili pepper varieties exhibited higher fruit length, diameter, weight, and yield values. There were increased contents of total phenol, flavonoids, and tannins in both fruit varieties with N2 application. Moreover, the placenta weights of the bottom, middle, and top layers of the fruits on the 35th day were improved by 40.14%, 26.80%, and 55.91% for ‘Honglong 23’ and 55.10%, 37.04%, and 75.56% for ‘Hongxi’ compared with N0. At the same time, under N2 treatment, the phenylalanine ammonia-lyase (PAL) enzyme activity of capsaicin synthase significantly increased. In contrast, the capsaicin-degrading enzyme activities of peroxidase (POD) and polyphenol oxidase (PPO) decreased notably. The expression levels of capsaicin-synthetic genes such as phenylalanine cleavage enzyme gene (PAL), acyltransferase gene (AT3), 4-Coumaroyl coenzyme A ligase gene (4CL), cinnamate 4-hydroxylase gene (C4H), caffeoyl coenzyme A-3-oxo-methyltransferase gene (COMT), paminotransferase gene (PAMT), and hydroxycinnamyltransferase gene (HCT) were up-regulated in N2 fruits, which led to a significant increase in capsaicin content compared with the other four nitrogen amounts. A further reduction in nitrogen application to N3 and N4 resulted in a decrease in the precursor substance’s total phenol content and PAL activity and an increase in the competitive substance’s flavonoid, lignin, POD, and PPO enzyme activities. At the same time, the expression levels of capsaicinoid synthetic genes were downregulated in the N3 and N4 treatments, leading to a low content of total capsaicinoids. The capsaicinoid content showed a trend of 35th day > 50th day > 20th day for both varieties. Additionally, the contents of total capsaicinoids, total phenols, flavonoids, and lignins, as well as PAL enzyme activity, and the expression levels of PAL, AT3, 4CL, C4H, COMT, PAMT, and HCT exhibited characteristics of bottom layers > middle layers > top layers. The activities of POD and PPO gradually increased from the bottom to the top layers. The N2 (562.5 kg ha−1) treatment resulted in increases in placenta mass, maximum capsaicinoid precursor substance of total phenol content, and synthase enzyme activity, as well as decreases in capsaicinoid competing substances and degradative enzyme activity, so there were more substances available for capsaicin synthesis. Combined with the higher fruit weight and capsaicinoid content in the N2 treatment, N2 was considered a suitable nitrogen fertilizer dosage for highly spicy chili pepper cultivation.

Abstract Studies on 3D-printed porous bone scaffolds mostly focus on materials or structural parameters, while the repair of large femoral defects needs to select appropriate structural parameters according to the needs of different parts. In this paper, a kind of stiffness gradient scaffold design idea is proposed. Different structures are selected according to the different functions of different parts of the scaffold. At the same time, an integrated fixation device is designed to fix the scaffold. Finite element method was used to analyze the stress and strain of homogeneous scaffolds and the stiffness gradient scaffolds, and the relative displacement and stress between stiffness gradient scaffolds and bone in the case of integrated fixation and steel plate fixation. The results showed that the stress distribution of the stiffness gradient scaffolds was more uniform, and the strain of host bone tissue was changed greatly, which was beneficial to the growth of bone tissue. The integrated fixation method is more stable, less stress and evenly distributed. Therefore, the integrated fixation device combined with the design of stiffness gradient can repair the large femoral bone defect well.

The stability of the forming layer shape is a critical factor that impacts the final quality of sample morphology in wire and arc additive manufacturing (WAAM). This paper investigates the forming process and control methods to identify ways to optimize the process and improve the quality of the final product. The study aims to enhance the quality and precision of samples produced by the WAAM process by establishing a morphology control method based on image feedback. The focus is on real-time image acquisition using a CCD and simultaneous extraction of forming process parameter characteristics. Using a central composite experimental design, a prediction model is developed to estimate key process parameters and feature sizes of deposits, including deposition height and width. To further analyze the dynamic characteristics of feature sizes of deposited layers, step response identification is conducted using three process parameters as the system input: forming speed, arc current, and wire feeding speed. The experiments are designed to determine the weight of each process parameter and achieve a high level of response speed and precision. The findings indicate that the feature size of the deposited layer is most sensitive to changes in forming speed, followed by wire feeding speed, while arc current has the least impact on feature size. After verification, the monitoring of the deposition height and width was found to be in good agreement with the prediction model, with an accuracy of over 90%. The results of this study can be used for size measurement and optimization of large aviation aluminum alloy components using WAAM technology and to improve the quality of products produced using these processes.

AbstractRecent enhancements in computational capacity provide an opportunity for harnessing the enormous amount of reservoir data already acquired and extracting useful information for hydrocarbon exploration, development, and production. This article reports a three-step clustering technique to determine well groups based on subsurface geological heterogeneity using feature extraction, hierarchical ensemble clustering, and spatial mapping. The first step of the presented methodology is to group the wells into different clusters based on the formation rock composition and property features extracted from well logs using the expectation maximization algorithm. The one-dimensional (1D) stacking pattern of each well log curve is expressed through a two-dimensional (2D) transformation scheme. Thus, the clustering can capture the vertical stacking patterns of well logs, which is essential for reservoir heterogeneity characterization. This base clustering process generated a feature matrix which is further grouped through the hierarchical ensemble clustering in a latent space of well logs in the second step. Through the ensemble clustering, different clustering proposals obtained from the base clustering are integrated corroboratively to reflect a comprehensive feature of all studied logs. In the third step, the spatial clustering is performed based on the ensemble results, considering the spatial distances between well locations in the target area. The results of the 2D spatial map may provide insights into the sedimentary depositional environment in terms of the lateral geological heterogeneity features. Therefore, the proposed clustering technique can present a fast geological modeling method to integrate geological heterogeneity features presented in multiple well logs, which is not yet fully utilized in traditional geomodeling approaches. The results can also support further reservoir studies, such as petrophysical modeling, reservoir modeling, and fluid flow simulation studies.

Fires in university laboratories often lead to serious casualties and property damage, and traditional sensor-based fire detection techniques suffer from fire warning delays. Current deep learning algorithms based on convolutional neural networks have the advantages of high accuracy, low cost, and high speeds in processing image-based data, but their ability to process the relationship between visual elements and objects is inferior to Transformer. Therefore, this paper proposes an improved YOLOX target detection algorithm combining Swin Transformer architecture, the CBAM attention mechanism, and a Slim Neck structure applied to flame smoke detection in laboratory fires. The experimental results verify that the improved YOLOX algorithm has higher detection accuracy and more accurate position recognition for flame smoke in complex situations, with APs of 92.78% and 92.46% for flame and smoke, respectively, and an mAP value of 92.26%, compared with the original YOLOX algorithm, SSD, Faster R-CNN, YOLOv4, and YOLOv5. The detection accuracy is improved, which proves the effectiveness and superiority of this improved YOLOX target detection algorithm in fire detection.