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A palladium-catalyzed [4 + 2] cycloaddition of amido-tethered allylic carbonates with oxazol-5-(4H)-ones proceeded smoothly under mild reaction conditions to give various piperidine derivatives in moderate to excellent yields.

The air distribution and speed uniformity of the cleaning fan in the cleaning room have a great influence on the working quality of the cleaning system of the harvester. In view of the problem of uneven air distribution in the cleaning room caused by improper adjustment of the main operating parameters of the cleaning fan in the cleaning device of the corn combine harvester, this paper takes the self-developed air screen cleaning test bench as the object. The main working parameters of the cleaning centrifugal fan (air supply distance, fan speed, and number of blades) were simulated and the Fluent simulation software was used to carry out the single-factor and multi-factor optimization tests, explore the influence law of each test factor on the air velocity in front of the screen, in the middle and behind the screen and the deviation degree of the airflow at the back of the screen surface, and find the optimal parameter combination. The data were systematically analyzed by multiple regression method and variance analysis method. The regression model of air velocity at the front, middle, and back of the screen and the air deviation degree at the back of the screen surface for the three working parameters of the cleaning fan were established. The optimal working parameter combination was obtained, that is, when the air supply distance is 580 mm, the fan speed is 1000 r/min, and the number of blades is 10, the airflow velocity in front of the screen is 10.8 m/s, the airflow velocity in the middle of the screen surface is 11.8 m/s, the airflow velocity at the back of the screen surface is 11.2 m/s, and the airflow deviation degree at the back of the screen surface is 13.5%. The relative errors were 1.9%, 0, 2.8%, and 3.0%, respectively. A combined test of the fan and the cleaning screen body with a feeding capacity of 8 kg/s was carried out, and the loss rate was 1.15% and the impurities rate was 1.24%. The regression model was reliable, and the optimal operation parameter combination performed well, meeting the technical requirements of cleaning operation, and providing theoretical guidance for the adjustment of fan structure and operation parameters in the cleaning system of the grain harvester.

Accurately and effectively measuring the breaking quality of harvested corn kernels is a critical step in the intelligent development of corn harvesters. The detection of broken corn kernels is complicated during the harvesting process due to turbulent corn kernel movement, uneven lighting, and interference from numerous external factors. This paper develops a deep learning-based detection method in real time for broken corn kernels in response to these issues. The system uses an image acquisition device to continuously acquire high-quality corn kernel image data and cooperates with a deep learning model to realize the rapid and accurate recognition of broken corn kernels. First, we defined the range of broken corn kernels based on image characteristics captured by the acquisition device and prepared the corn kernel datasets. The corn kernels in the acquired image were densely distributed, and the highly similar features of broken and whole corn kernels brough challenges to the system for visual recognition. To address this problem, we propose an improved model called BCK-YOLOv7, which is based on YOLOv7. We fine-tuned the model’s positive sample matching strategy and added a transformer encoder block module and coordinate attention mechanism, among other strategies. Ablation experiments demonstrate that our approach improves the BCK-YOLOv7 model’s ability to learn effectively broken corn kernel features, even when high-density features are similar. The improved model achieved a precision rate of 96.9%, a recall rate of 97.5%, and a mAP of 99.1%, representing respective improvements of 3.7%, 4.3%, and 2.8% over the original YOLOv7 model. To optimize and deploy the BCK-YOLOv7 model to the edge device (NVIDIA Jetson Nano), TensorRT was utilized, resulting in an impressive inference speed of 33 FPS. Finally, the simulation system experiment for corn kernel broken rate detection was performed. The results demonstrate that the system’s mean absolute deviation is merely 0.35 percent compared to that of manual statistical results. The main contribution of this work is the fact that this is the first time that a set of deep learning model improvement strategies and methods are proposed to deal with the problem of rapid and accurate corn kernel detection under the conditions of high density and similar features.

The gaseous reactive oxygen/nitrogen species (RONS) generated by cold atmospheric plasma (CAP) can effectively inactivate Aspergillus flavus (A. flavus) and prolong the shelf-life of food. Plasma-activated water (PAW) is the extension of cold plasma sterilization technology. Without the limitation of a plasma device, PAW can be applied to more scenarios of food decontamination. However, the efficacy of PAW as a carrier of RONS for eradicating A. flavus or inhibiting its growth remains unclear. In this study, the immediate fungicidal effect and long-term inhibitory effect of PAW on A. flavus were investigated. The results demonstrated that 60-min instant-prepared PAW could achieve a 3.22 log reduction CFU/mL of A. flavus and the fungicidal efficacy of PAW gradually declined with the extension of storage time. Peroxynitrite (ONOO−/ONOOH) played a crucial role in this inactivation process, which could damage the cell wall and membrane structure, disrupt intracellular redox homeostasis, and impair mitochondrial function, ultimately leading to fungal inactivation. In addition to the fungicidal effect, PAW also exhibited fungistatic properties and inhibited the synthesis of aflatoxin B1 (AFB1) in A. flavus. By analyzing the cellular antioxidant capacity, energy metabolism, and key gene expression in the AFB1 synthesis pathway, it was discovered that PAW can significantly reduce ATP levels, while increasing SOD and CAT activity during 5-d cultivation. Meanwhile, PAW effectively suppressed the expression of genes related to AFB1 synthesis.

Based on the mechanical test (shear test, compression test), the bond model of corn kernel and straw was established to explore the rolling and crushing effect of different crushing rollers. The type of crushing roller is different. The material crushing process by the force (extrusion and kneading) is different. The mechanical analysis of the crushing process reveals that the disc crushing roller (DCR) has the characteristics of large unit-length kneading area; the spiral-notched serrated crushing roller (SNSCR) has transverse shearing effect on the material; and they affect the crushing effect of the material. By means of discrete element method and simulation test, multiple regression method and variance analysis method are used to systematically analyze the data. The optimal working parameters of each roll (crushing roll speed, crushing clearance, differential ratio) were obtained. The simulation test and bench test of the crushing process of materials with different roll shapes were carried out under the optimal working parameters. The crushing effect was evaluated with a Binzhou screen and a corn silage grain-crushing score screen. The crushed materials of corn kernel can be divided into three categories according to the size (broken grains passed through 2 mm sieve; broken grains passed through 4.75 mm sieve; and broken grains that cannot pass through 4.75 mm sieve), and the crushed materials of corn stalk can be divided into four categories according to the size and thickness (broken straw through 4 mm sieve; broken straw through 8 mm sieve; broken straw through 19 mm sieve; and broken straw that cannot pass 19 mm sieve). The crushing effect and crushing classification of the simulation test and bench test were basically consistent. The results showed that the disc crushing roller group had the highest comprehensive score with straw rolling rate of 89.1% and grain crushing rate of 87.7%, which was the most suitable for harvesting whole-plant silage maize (WSM).

Toll/IL-1R domain-containing adaptor-inducing IFN-β (TRIF), tumor necrosis factor receptor-associated factor 6 (TRAF6) and TANK-binding kinase 1 (TBK1) are critical signal transducers in toll-like receptors (TLRs) signaling pathway. In the present study, TRIF, TRAF6 and TBK1 were characterized from golden pompano (Trachinotus ovatus), named as TroTRIF, TroTRAF6 and TroTBK1, respectively. The full cDNA length of TroTRIF, TroTRAF6 and TroTBK1 was 2297 bp, 2293 bp, and 2482 bp, which respectively encoded 589, 573 and 723 amino acids. The deduced amino acids sequences of TroTRIF, TroTRAF6 and TroTBK1 contained conserved motifs, similar to their counterparts in other vertebrates. Phylogenetic tree analysis revealed that TroTRIF, TroTRAF6 and TroTBK1 were well clustered with their counterparts in other fish species. Quantitative Real-Time PCR (qPCR) analysis showed that TroTRIF, TroTBK1 and TroTRAF6 were detected in all examined tissues of healthy fish, but shared distinct transcript levels. Moreover, the expressions of TroTRIF, TroTBK1 and TroTRAF6 were generally induced by polyriboinosinic-polyribocytidylic acid (polyI:C), lipopolysaccharide (LPS), and Vibrio alginolyticus stimulation in vivo, indicating their critical roles in the immune defense of golden pompano against pathogen invasion. Our results provide valuable information for understanding the functions of these genes in golden pompano.

Bridge load testing is crucial in evaluating bridge load-carrying capacity and construction quality. However, conventional techniques have many limitations such as the need for direct contact and low acquisition frequency. This study provides a comprehensive examination of the static and dynamic load tests of a multi-span cable-stayed bridge utilizing a ground-based radar inter-ferometer (GB-radar). The case study was conducted at the Fifth Nanjing Yangtze River Bridge (FNYRB), which is recognized as the world's first lightweight steel-concrete composite cable-stayed bridge, with two spans measuring 600 m. To enhance measurement accuracy, a method was proposed to detect and recover radar phase jumps caused by vehicle motion. Furthermore, precise geometry projection was employed to acquire vertical displacements from GB-radar which were taken along the line-of-sight (LOS) direction. Moreover, during the static load test, a continuous deformation was observed with a maximum pace of 0.31 mm/min, indicating a post-construction settlement caused by soft soil consolidation. This case study highlights the high sampling frequency, high measurement accuracy, and exceptional weatherproof ability of GB-radar, thereby demonstrating its potential to be an alternative to the structural health monitoring (SHM) system.

Non-utility owned distributed energy resources (DERs) are mostly untapped currently, but they can provide many grid services such as voltage regulation and service restoration, if properly controlled, and can improve the distribution systems reliability when coordinated with utility-owned assets such as self-healing control and microgrids. This paper integrates transactive energy control into the distribution system reliability evaluation to quantitatively assess the impact of non-utility owned DERs on reliability improvement. A transactive reactive power control strategy is designed to incentivize the DERs to provide reactive power support for improving voltage profiles thus enabling additional customer load restoration during an outage. Also, an operational sequence to coordinate the non-utility owned DERs with the utility owned self-healing control and utility owned microgrids is designed and integrated into the service restoration process with the operational constraints guaranteed by checking the three-phase unbalanced power flow for post-fault network reconfiguration. The reliability indices are then calculated through a Monte Carlo simulation. The transactive reactive power control strategy is tested on a four-feeder distribution system operated by Duke Energy in the U.S. Results demonstrate that the non-utility owned DERs with the transactive control improve the reliability of both the system and critical loads by more than 30%.