3,4-Bis(4-meth­oxy­phen­yl)-2,5-dihydro-1H-pyrrole-2,5-dione

In the title compound, C18H15NO4, the benzene rings form quite different dihedral angles [16.07 (1) and 59.50 (1)°] with the central pyrrole ring, indicating a twisted mol­ecule. Conjugation is indicated between the five- and six-membered rings by the lengths of the C—C bonds which link them [1.462 (3) and 1.477 (3) Å]. The most prominent feature of the crystal packing is the formation of inversion dimers via eight-membered {⋯HNCO}2 synthons

Design of UDE-based dynamic surface control for dynamic positioning of vessels with complex disturbances and input constraints

In practice, dynamic positioning (DP) vessels are subjected to complex disturbances as well as the magnitude and changing rate constraints of the thrusts and moments. This study applied a dynamic surface controller based on an uncertainty and disturbance estimator (UDE) to a DP vessel with complex disturbances and input constraints. The UDE was designed to estimate and handle the complex disturbances. An auxiliary dynamic system (ADS) and smooth switching function were employed to compensate for the input constraints and avoid the singularity phenomenon caused by the ADS, respectively. The combination of the UDE method and dynamic surface control (DSC) technology significantly simplified the design process for the control law and increased the practicability for DP vessels. The stability of the proposed control law was proved using the Lyapunov theory. The effectiveness of the control law and possibility of actually applying it to a DP vessel were verified using simulation experiments

AgriPest-YOLO: A rapid light-trap agricultural pest detection method based on deep learning

Light traps have been widely used for automatic monitoring of pests in the field as an alternative to time-consuming and labor-intensive manual investigations. However, the scale variation, complex background and dense distribution of pests in light-trap images bring challenges to the rapid and accurate detection when utilizing vision technology. To overcome these challenges, in this paper, we put forward a lightweight pest detection model, AgriPest-YOLO, for achieving a well-balanced between efficiency, accuracy and model size for pest detection. Firstly, we propose a coordination and local attention (CLA) mechanism for obtaining richer and smoother pest features as well as reducing the interference of noise, especially for pests with complex backgrounds. Secondly, a novel grouping spatial pyramid pooling fast (GSPPF) is designed, which enriches the multi-scale representation of pest features via fusing multiple receptive fields of different scale features. Finally, soft-NMS is introduced in the prediction layer to optimize the final prediction results of overlapping pests. We evaluated the performance of our method on a large scale multi pest image dataset containing 24 classes and 25k images. Experimental results show that AgriPest-YOLO achieves end-to-end real-time pest detection with high accuracy, obtaining 71.3% mAP on the test dataset, outperforming the classical detection models (Faster RCNN, Cascade RCNN, Dynamic RCNN,YOLOX and YOLOv4) and lightweight detection models (Mobilenetv3-YOLOv4, YOLOv5 and YOLOv4-tiny), meanwhile our method demonstrates better balanced performance in terms of model size, detection speed and accuracy. The method has good accuracy and efficiency in detecting multi-class pests from light-trap images which is a key component of pest forecasting and intelligent pest monitoring technology

Changes of Microorganisms and Flavor Compounds in the Fourth Round Jiupei of Sauce-flavored Baijiu in Beijing

Sauce-flavor Baijiu shows a typical sauce flavor, with a delicate mouthfeel. Its fermentation process consists of seven cycles, and each cycle produces one kind of base liquor. The fourth cycle liquor has a rich flavor and excellent quality. During the fermentation process, microorganisms are in a dynamic state, while flavor substances differ. This study investigated the microbial changes of Jiupei in the fourth cycle and their impact on flavor substances. Species diversity analysis was conducted on the fourth cycle Jiupei sample using high-throughput sequencing. Flavor substances were analyzed using headspace solid-phase microextraction combined with gas chromatography-mass spectrometry. Results showed that the dominant bacteria in the Jiupei were Lactobacillus, Virgibacillus, and Kroppenstedtia, while dominant fungi were Thermoascus, Aspergillus, and Issatchenkia. The microbial community in the Jiupei showed significant dynamic changes during the later stage of fermentation. The Jiupei showed the richest variety of alcohol and ester substances at the beginning of fermentation, with the relative content of alcohol, ester, and acid compounds showing a pattern of increasing firstly and then gradually decreased during fermentation. This study conducted a correlation analysis between fungi and bacteria at the genus level and flavor substances, revealing that Monascus, Lactobacillus, and Wickerhamomyces were positively correlated with key flavor substances, such as ethyl acetate, ethyl lactate, and ethyl hexanoate, respectively. The data provided a basis for comparing the microorganisms and flavor substances in the fourth cycle of sauce-flavor Baijiu, and offered a theoretical basis for improving the quality of Baijiu

A path planning strategy unified with a COLREGS collision avoidance function based on deep reinforcement learning and artificial potential field

Improving the autopilot capability of ships is particularly important to ensure the safety of maritime navigation.The unmanned surface vessel (USV) with autopilot capability is a development trend of the ship of the future. The objective of this paper is to investigate the path planning problem of USVs in uncertain environments, and a path planning strategy unified with a collision avoidance function based on deep reinforcement learning (DRL) is proposed. A Deep Q-learning network (DQN) is used to continuously interact with the visually simulated environment to obtain experience data, so that the agent learns the best action strategies in the visual simulated environment. To solve the collision avoidance problems that may occur during USV navigation, the location of the obstacle ship is divided into four collision avoidance zones according to the International Regulations for Preventing Collisions at Sea (COLREGS). To obtain an improved DRL algorithm, the artificial potential field (APF) algorithm is utilized to improve the action space and reward function of the DQN algorithm. A simulation experiments is utilized to test the effects of our method in various situations. It is also shown that the enhanced DRL can effectively realize autonomous collision avoidance path planning