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The absorption heat exchanger with a large temperature difference has a higher heat transfer superiority than the other heat exchangers (including plate heat exchanger), which is more suitable for long-distance heating. To improve its system performance, parameter collaborative optimization (including building accurate predictive models) has become an effective method because it does not require too much investment. In this study, a heat exchange station was chosen as a case study, and a model of a long short-term memory (LSTM) neural network was used to predict the temperatures of primary return water and secondary return water. Accordingly, the reliability of the fitting result based on the model was confirmed through a contrastive analysis with the prediction results of a support vector machine (SVM) model, a random forest (RF) model, and an extreme gradient boosting (XGBoost) model. In addition, the algorithm of particle swarm optimization was used to optimize the flow rate of primary supply water. The results showed that the temperature of primary-side return water decreased from 29.6 °C to 28.2 °C, the temperature of secondary-side return water decreased from 39.8 °C to 38.6 °C, and the flow rate of primary-side supply water decreased from 39 t/h to 35.2 t/h after the optimization of the flow rate of primary supply water. The sensibility assessment emerged that the secondary-side flow rate to the secondary-side supply water temperature was about 7 times more sensitive than the primary-side supply water temperature, and concretely, the lower the temperature, the higher the sensibility. In summary, the accuracy of the proposed prediction model was validated and the optimization direction was pointed out, which can be used to provide guidance for designing and planning absorption heat exchange stations with large temperature differences. [ABSTRACT FROM AUTHOR]

Building formwork is a kind of temporary supporting structure consumable material used in the construction field. In recent years, building formwork has gradually developed to become lighter, more environmentally friendly, and have higher performance. This sets higher requirements for the materials used to make building formwork. There is an urgent need to find a lighter and more durable material for building formwork. Magnesium alloys possess the advantages of low density, high alkali resistance, and high strength. As a building formwork material, it can reduce the weight of formwork and improve its durability. Therefore, a magnesium alloy is considered a material with high potential for building formwork. Currently, magnesium alloy building formwork has attracted the attention of many companies and research and development institutions, with preliminary research applications and good feedback on usage effects. It is highly possible to obtain the opportunity to put it into market application. However, to be applied on a large scale, there are still some important problems that need to be solved. These problems fall into three main areas, including the relatively low processing efficiency of magnesium alloy materials, the unstable price of magnesium alloys, and the fact that the formwork is easily corroded during storage. Firstly, at present, the main processing methods for magnesium alloy building formwork are casting and extrusion, and the production efficiency of both methods needs to be improved. Secondly, high-performance magnesium alloy materials are usually more expensive, which is not conducive to the large-scale application of the formwork. The price of magnesium alloys has fluctuated greatly in recent years, which increases the difficulty of promoting magnesium alloy building formwork. Thirdly, in the atmosphere, the oxide film on the surface of the magnesium alloy cannot play an effective role in corrosion resistance. So, surface treatment is necessary for magnesium alloy building formwork. Among the various surface treatment methods for magnesium alloys, the chemical conversion method has the advantages of being easy to operate, cost-effective, and having good corrosion resistance. It may be a very suitable protective method for large-scale applications of magnesium alloy building formwork and possesses excellent potential for application. The future of magnesium alloy building formwork will focus on new low-cost materials, high-efficiency processing technology, and low-cost green anti-corrosion technology. With in-depth research and the maturation of technology, magnesium alloy formwork is expected to play a more important role in the construction industry. [ABSTRACT FROM AUTHOR]

Interaction between transcription factors (TFs) and motifs is essential for gene regulation and the subsequent phenotype formation. Soybean (Glycine max) JAGGEED 1 (GmJAG1) is a key TF that controls leaf shape, seed number and flower size. To understand the GmJAG1 binding motifs, in this study, we performed the GmJAG1 DNA affinity purification sequencing (DAP-seq) experiment, which is a powerful tool for the de novo motif prediction method. Two new significant GmJAG1 binding motifs were predicted and the EMSA experiments further verified the ability of GmJAG1 bound to these motifs. The potential binding sites in the downstream gene promoter were identified through motif scanning and a potential regulatory network mediated by GmJAG1 was constructed. These results served as important genomic resources for further understanding the regulatory mechanism of GmJAG1. [ABSTRACT FROM AUTHOR]

The high consumption and high cost of cement are the bottleneck problems that limit the development of cemented tailings backfilling technology. The low-consumption cement backfill is immersed in a weak acid/alkaline groundwater environment for a long time. Reducing the consumption of cement can easily lead to problems such as a sudden decrease in strength and the leakage of heavy metals. Through the monolithic leaching test in static and uniaxial compressive tests, the heavy metals' leaching concentration and the compressive strength of low-consumption cement backfills in different pH soaking solutions were measured at different soaking times. Results show that a lower cement concentration will result in a lower CTB compressive strength and a higher rate of heavy metal leaching. Long-term exposure to an acidic/alkaline environment will lead to the instability and destruction of the CTB structure. A microscopic examination reveals that the creation of hydration products can improve the structure's compactness while also lowering the internal porosity of CTB but can also solidify heavy metal ions in various ways. A first-order reaction/diffusion model (FRDM) can better evaluate the leaching behavior of CTB. This study helps to improve backfilling technology, thereby contributing to the creation of sustainable mining geotechnologies. [ABSTRACT FROM AUTHOR]

In the realm of automated apple picking operations, the real-time monitoring of apple maturity and diameter characteristics is of paramount importance. Given the constraints associated with feature detection of apples in automated harvesting, this study proposes a machine vision-based methodology for the accurate identification of Fuji apples' maturity and diameter. Firstly, maturity level detection employed an improved YOLOv5s object detection model. The feature fusion section of the YOLOv5s network was optimized by introducing the cross-level partial network module VoVGSCSP and lightweight convolution GSConv. This optimization aimed to improve the model's multiscale feature information fusion ability while accelerating inference speed and reducing parameter count. Within the enhanced feature fusion network, a dual attention mechanism combining channel and spatial attention (GAM) was introduced to refine the color and texture feature information of apples and to increase spatial position feature weights. In terms of diameter determination, the contours of apples are obtained by integrating the dual features of color and depth images within the target boxes acquired using the maturity detection model. Subsequently, the actual area of the apple contour is determined by calculating the conversion relationship between pixel area and real area at the current depth value, thereby obtaining the diameter of the apples. Experimental results showed that the improved YOLOv5s model achieved an average maturity level detection precision of 98.7%. Particularly noteworthy was the detection accuracy for low maturity apples, reaching 97.4%, surpassing Faster R-CNN, Mask R-CNN, YOLOv7, and YOLOv5s models by 6.6%, 5.5%, 10.1%, and 11.0% with a real-time detection frame rate of 155 FPS. Diameter detection achieved a success rate of 93.3% with a real-time detection frame rate of 56 FPS and an average diameter deviation of 0.878 mm for 10 apple targets across three trials. Finally, the proposed method achieved an average precision of 98.7% for online detection of apple maturity level and 93.3% for fruit diameter features. The overall real-time inference speed was approximately 56 frames per second. These findings indicated that the method met the requirements of real-time mechanical harvesting operations, offering practical importance for the advancement of the apple industry. [ABSTRACT FROM AUTHOR]