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Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection. However, previous works have tried to improve the performance in various object detection necks but have failed to extract features efficiently. To solve the insufficient features of objects, this work introduces some of the most advanced and representative network models based on the Faster R-CNN architecture, such as Libra R-CNN, Grid R-CNN, guided anchoring, and GRoIE. We observed the performance of Neighbour Feature Pyramid Network (NFPN) fusion, ResNet Region of Interest Feature Extraction (ResRoIE) and the Recursive Feature Pyramid (RFP) architecture at different scales of precision when these components were used in place of the corresponding original members in various networks obtained on the MS COCO dataset. Compared to the experimental results after replacing the neck and RoIE parts of these models with our Reinforced Neighbour Feature Fusion (RNFF) model, the average precision (AP) is increased by 3.2 percentage points concerning the performance of the baseline network.

With continuous fossil energy consumption and global warming, renewable energy has become more popular. Therefore, more and more researchers have begun to study hydrogen-based hybrid renewable energy systems. This article mainly summarizes the commercial software tools and the simulation of hybrid renewable energy systems in recent years. Firstly, the commonly used softwares and research methods are reviewed so that other researchers can choose suitable tools and strategies to conduct their researches. Then TRNSYS software is used to perform a case study in Yichang, Hubei, China. The established model is simulated according to the one-year load of the community. The simulation results show that the total power generation of the system can meet the load demand most of the time but is insufficient in some peak periods. The system can further be optimized using optimization methods to fully meet the load.

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This paper develops a system dynamics framework for the closed-loop agri-food brand supply chain (AFBSC) with multiple small farmer suppliers and one core brand manufacturer, and investigates the influences of various factors including brand effort, quality elasticity, price elasticity, revenue sharing, and the number of suppliers on the system behavior. The results show: (i) food quality is determined by all farmer suppliers, who might choose hitchhiking with the prisoner’s dilemma game in a decentralized decision-making mode; (ii) brand effort to improve brand value for food quality is mainly made by the core brand manufacturer, who presents a goal-seeking system dynamics (SD) manner with oscillation behavior around the expected quality of consumers; (iii) whether farmer suppliers or brand manufacturers, the centralized decision-making mode is more useful for them to increase revenue than the decentralized one; furthermore, the shared centralized decision-making mode is most useful for them to obtain more revenue, and the brand manufacturer is still the biggest beneficiary.

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