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Plant diseases caused by pathogenic fungi seriously affect the yield and quality of crops, cause huge economic losses, and pose a considerable threat to global food security. Phenylpyrrole analogues were designed and synthesized based on alkaloid lycogalic acid. All target compounds were characterized by 1H NMR, 13C NMR, and HRMS. Their antifungal activities against seven kinds of phytopathogenic fungi were evaluated. The results revealed that most compounds had broad-spectrum fungicidal activities at 50 μg/mL; 14 compounds displayed more than 60% fungicidal activities against Rhizoctonia cerealis and Sclerotinia sclerotiorum, and in particular, the fungicidal activities of compounds 8g and 8h against Rhizoctonia cerealis were more than 90%, which could be further developed as lead agents for water-soluble fungicides. The molecular docking results indicate that compounds 8g and 8h can interact with 14α-demethylase (RcCYP51) through hydrogen bonding with strong affinity.

In this article, we use a low-power iBeacon network to conduct an in-depth analysis and research on the principle of indoor positioning and adopt an efficient and fast positioning algorithm. Secondly, based on the obtained analysis, an iBeacon-based indoor positioning system is proposed to analyze how to use iBeacon for accurate positioning and whether it can effectively compensate for the current mainstream positioning system. We analyze the requirements of the iBeacon-based indoor positioning system and propose the design of this positioning system. We analyze the platform and environment for software development, design the general framework of the positioning system, and analyze the logical structure of the whole system, the structure of data flow, and the communication protocols between each module of the positioning system. Then, we analyze the functions of the server module and the client module of the system, implement the functions of each module separately, and debug the functions of each module separately after each module is implemented. The feasibility of the algorithm and the performance improvement are confirmed by the experimental data. Our results show that the communication distance is improved by approximately 20.25% and the accuracy is improved by 5.62% compared to other existing results.

The International Telecommunication Union (ITU) proposed the very high frequency data exchange system (VDES) to improve the efficiency of ship–ship and ship–shore communication; however, its existing single-hop transmission mode is insufficient for identifying all ships within a coastal zone. This paper proposes an adaptive cooperative ship identification method based on the VDES using multihop transmission, where the coastal zone is divided into a grid, with the ships acting as nodes, and the optimal sink and relay nodes are calculated for each grid element. An adaptive multipath transmission protocol is then applied to improve the transmission efficiency and stability of the links between the nodes. Simulations were performed utilizing real Automatic Identification System (AIS) data from a coastal zone, and the results showed that the proposed method effectively reduced the time-slot occupancy and collision rate while achieving a 100% identification of ships within 120 nautical miles (nm) of the coast with only 4.8% of the usual communication resources.

Sparse representation of signals based on a redundant dictionary is a new signal representation theory. Recent research activities in this field have concentrated mainly on the study of dictionary design and sparse decomposition algorithms. Currently, the application of sparse representation on an Automatic Identification System (AIS) signal still requires further investigations. In this paper, a novel sparse representation of the AIS signal is proposed based on an adaptive redundant dictionary. Considering the characteristics of the AIS signal, an adaptive redundant dictionary is constructed using the K singular value decomposition (K-SVD) algorithm. Furthermore, an effective pursuit algorithm is proposed to obtain the sparse representation of AIS signal using the adaptive dictionary. The binary AIS message is demodulated from the sparse representation of AIS signal. The experimental results indicate that the sparse representation of the AIS signal has high accuracy and the reconstructive error rate can be under 10%; thus, the reconstructive precision is simultaneously guaranteed. The processing time of the proposed sparse representation algorithm is less than 26.7 ms which satisfies the requirements of AIS real-time signal processing. It shows that introducing the signal sparse representation in a real-time signal system obtains a satisfactory result.

Creep is one of the primary degradation mechanisms affecting the performance of the 800H alloy under long-term high-temperature stress conditions. Understanding the microstructural evolution during creep and developing a quantitative model to relate these changes to mechanical properties are essential for assessing creep damage and ensuring the safe operation of high-temperature equipment. By conducting a multiscale quantitative characterization of the microstructures in the 800H alloy across different creep stages, we systematically examined the evolution of various microstructural features and their influence on Young’s modulus. A quantitative prediction model of Young’s modulus based on microstructural characteristics was developed, achieving a prediction accuracy exceeding 95% with a mean absolute percentage error of just 1.59% compared to experimental values. This work not only elucidates the intrinsic relationship between microstructural features and macroscopic mechanical properties but also provides a foundation for the in-service creep damage assessment of high-temperature components.

Nanoscale Cu-rich precipitates (CRPs) play a crucial role in the irradiation embrittlement of reactor pressure vessels (RPVs), and binary Fe-Cu alloys serve as practical models to study the evolution of these precipitates. This study investigates the electrical resistivity of an Fe-1.17 wt.% Cu model alloy aged at 450 °C to enhance the understanding of electrical measurements for the non-destructive assessment of RPV irradiation embrittlement. Multi-level characterization methods were used to obtain quantitative data on multi-scale microstructures, including precipitates, dislocations, and grains. The formation and growth of CRPs were found to align closely with the Johnson–Mehl–Avrami model, and the variation in electrical resistivity showed a strong correlation with the evolution of the microstructure. Combined with detailed quantitative microstructure evolution analysis, an electrical resistivity prediction model that considers microstructural mechanisms has been developed. This model can accurately show the effect of CRPs on resistivity and can potentially be extended to RPV steels with other solute-rich precipitates, with a maximum absolute percentage error not exceeding 5%. These results provide a robust basis for the non-destructive and in-service evaluation of RPV irradiation embrittlement using electrical resistivity.

To use the existing Automatic Identification System (AIS) shore stations for positioning so that the AIS can be used as an additional land-based positioning system for coastal vessels is a cutting-edge research topic, responding to the call of the International Maritime Organization (IMO). In order to use the ship-borne AIS for positioning function, a holographic detection of AIS real-time signal based on sparse representation is presented in this paper. Considering the working environment and the requirement of AIS real-time signal processing, a novel fast noise resistance Orthogonal Matching Pursuit (OMP) algorithm is presented. Furthermore, the choice and detection of the timestamp of the reconstructed signal is analyzed and carried out which will be used in the ranging system. The experiment results indicate that the proposed fast noise resistance OMP algorithm can greatly reduce the processing time, and the difference in processing time increases with the number of iterations. The improvement in noise immunity is also obvious, and the error rate reduces at about 9% under the same SNR. The timestamp of the reconstructed signals can be detected successfully. It shows that the holographic detection of AIS real-time signal is achieved satisfactorily.

The research aims to investigate the mechanical response of footfalls at different velocities to understand the mechanism of heel injury and provide a scientific basis for the prevention and treatment of heel fractures. A three-dimensional solid model of foot drop was constructed using anatomical structures segmented from medical CT scans, including bone, cartilage, ligaments, plantar fascia, and soft tissues, and the impact velocities of the foot were set to be 2 m/s, 4 m/s, 6 m/s, 8 m/s, and 10 m/s. Explicit kinetic analysis methods were used to investigate the mechanical response of the foot landing with different speeds to explore the damage mechanism of heel bone at different impact velocities. Lower impact velocities result in relatively low stress on the medial cortex and posterior talar articular bony surfaces, which may result in minor injury or stress adaptation in the heel. As the impact velocity increases, the stresses on the medial cortex and posterior taller articular surface also increase significantly, greatly raising the risk of heel fractures. This study holds significant implications for safeguarding foot health and enhancing the safety of athletes and individuals engaged in high-impact sports.

Inhibiting the protein neddylation pathway using the NEDD8-activating enzyme inhibitor MLN4924 represents an attractive anticancer strategy having been demonstrated to exhibit promising anticancer efficacy and with tolerable levels of toxicity; however, the mechanism by which MLN4924 inhibits cell proliferation in human esophageal squamous cell carcinoma (ESCC) cells requires further investigation. The present study revealed that MLN4924 treatment led to G2 cell cycle arrest and enhanced the protein stability of Wee1-like protein kinase and cyclin dependent protein kinase inhibitor 1A and B and p27. Furthermore, MLN4924 induced DNA damage and sensitized esophageal cancer cells to cisplatin by enhancing apoptosis. These findings extend the understanding of the function and mechanism of MLN4924 in ESCC and provide further evidence for the future development of neddylation inhibitors in clinical trials of esophageal cancer therapy, either alone or in combination.

Inhibiting the protein neddylation pathway using the NEDD8-activating enzyme inhibitor MLN4924 represents an attractive anticancer strategy having been demonstrated to exhibit promising anticancer efficacy and with tolerable levels of toxicity; however, the mechanism by which MLN4924 inhibits cell proliferation in human esophageal squamous cell carcinoma (ESCC) cells requires further investigation. The present study revealed that MLN4924 treatment led to [G.sub.2] cell cycle arrest and enhanced the protein stability of Wee1-like protein kinase and cyclin dependent protein kinase inhibitor 1A and B and p27. Furthermore, MLN4924 induced DNA damage and sensitized esophageal cancer cells to cisplatin by enhancing apoptosis. These findings extend the understanding of the function and mechanism of MLN4924 in ESCC and provide further evidence for the future development of neddylation inhibitors in clinical trials of esophageal cancer therapy, either alone or in combination. Key words: esophageal squamous cell carcinoma, neddylation, MLN4924, Cullin-RING ligase