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Dwarf apple is one of the most important forms of garden economy, which has become a new engine for rural revitalization. The effective detection of coloring and ripening apples in complex environments is important for the sustainable development of smart agricultural operations. Addressing the issues of low detection efficiency in the greenhouse and the challenges associated with deploying complex target detection algorithms on low-cost equipment, we propose an enhanced lightweight model rooted in YOLOv7. Firstly, we enhance the model training performance by incorporating the Squeeze-and-Excite attention mechanism, which can enhance feature extraction capability. Then, an SCYLLA-IoU (SIoU) loss function is introduced to improve the ability of extracting occluded objects in complex environments. Finally, the model was simplified by introducing depthwise separable convolution and adding a ghost module after up-sampling layers. The improved YOLOv7 model has the highest AP value, which is 10.00%, 5.61%, and 6.00% higher compared to YOLOv5, YOLOv7, and YOLOX, respectively. The improved YOLOv7 model has an MAP value of 95.65%, which provides higher apple detection accuracy compared to other detection models and is suitable for potted dwarf anvil apple identification and detection. [ABSTRACT FROM AUTHOR]

This study utilized a plant-wearable sap flow sensor developed by a multidisciplinary team at Zhejiang University to monitor water distribution patterns in watermelon fruit stalks throughout their developmental stages. The dynamic rules of sap flow at different stages of fruit development were discovered: (1) In the first stage, sap flow into the fruit gradually halts after sunrise due to increased leaf transpiration, followed by a rapid increase post-noon until the next morning, correlating with fruit expansion. (2) In the second stage, the time of inflow sap from noon to night is significantly shortened, while the outflow sap from fruit is observed with the enhancement of leaf transpiration after sunrise, which is consistent with the slow fruit growth at this stage. (3) In the third stage, the sap flow maintains the diurnal pattern. However, the sap flow that inputs the fruit at night is basically equal to the sap flow that outputs the fruit during the day; the fruit phenotype does not change anymore. In addition, a strong correlation between the daily mass growth in fruit and the daily sap flow amount in fruit stalk was identified, validating the sensor's utility for fruit growth monitoring and yield prediction. [ABSTRACT FROM AUTHOR]

Drought, an intricate natural phenomenon globally, significantly influences the gross primary productivity (GPP) and carbon sink potential of tropical forests. Present research on the drought response primarily focuses on natural forests, such as the Amazon rainforest, with relatively limited studies on tropical plantations. Therefore, for a comprehensive understanding of global climate change, accurately evaluating and analyzing the sensitivity and vulnerability of rubber plantation GPP to various drought characteristics is crucial. The Standardized Precipitation Evapotranspiration Index (SPEI) was used in this research to quantify drought intensity. The Spatially Explicit Individual Based Dynamic Global Vegetation Model (SEIB-DGVM) was localized based on observation data from the Hainan Danzhou Tropical Agro-ecosystem National Observation and Research Station. Subsequently, the calibrated model was utilized to simulate the dynamic process of rubber plantation GPP under multi-gradient drought scenarios (2 extreme boundaries × 3 drought initiation seasons × 4 drought intensities × 12 drought durations × 12 SPEI time scales). The results show that the sensitivity and vulnerability of rubber plantation GPP exhibit significant differences under drought scenarios in different initiation seasons; GPP exhibits higher sensitivity to extreme, long-duration flash droughts in the early rainy season. Regarding vulnerability, the impact of extreme, long-duration flash droughts on GPP is most pronounced. This research lays the foundation for estimating the impact of droughts on the GPP of rubber plantations under future climate change scenarios, providing a scientific basis for enhancing regional ecological restoration and protection. [ABSTRACT FROM AUTHOR]

Topological phase transition materials have strong coupling between their charge, spin orbitals, and lattice structure, which makes them have good electrical and magnetic properties, leading to promising applications in the fields of memristive devices. The smaller Gibbs free energy difference between the topological phases, the stable oxygen vacancy ordered structure, and the reversible topological phase transition promote the memristive effect, which is more conducive to its application in information storage, information processing, information calculation, and other related fields. In particular, extracting the current resistance or conductance of the two-terminal memristor to convert to the weight of the synapse in the neural network can simulate the behavior of biological synapses in their structure and function. In addition, in order to improve the performance of memristors and better apply them to neuromorphic computing, methods such as ion doping, electrode selection, interface modulation, and preparation process control have been demonstrated in memristors based on topological phase transition materials. At present, it is considered an effective method to obtain a unique resistive switching behavior by improving the process of preparing functional layers, regulating the crystal phase of topological phase transition materials, and constructing interface barrier-dependent devices. In this review, we systematically expound the resistance switching mechanism, resistance switching performance regulation, and neuromorphic computing of topological phase transition memristors, and provide some suggestions for the challenges faced by the development of the next generation of non-volatile memory and brain-like neuromorphic devices based on topological phase transition materials. [ABSTRACT FROM AUTHOR]

In China, an effective form of agricultural organization called an agri-industrialized union (AIU) has been on the rise and is recognized as making a great contribution to rural revitalization. However, individual AIUs appear to be conducted in different ways during their development. Some are successfully operated by active members in cohesive combinations, while some fail because of disconnection and instability. The purpose of this study is to encourage all members to take initiative and to act cooperatively to ensure high-quality resource usage in AIUs. Based on the theory of planned behavior (TPB) and the theory of perceived value, a structural equation model was used to examine the main factors affecting members' willingness to cooperate in AIUs in Hebei Province. The data collected from a survey of 247 AIUs indicated that behavioral attitudes and subjective norms have a direct impact on cooperative initiatives. Perceived behavioral control indirectly affects cooperative initiatives. Perceived management ability influences subjective norms, while perceived interest–risk influences behavioral attitudes and perceived behavioral control, especially in hierarchical governance groups. The influences of behavioral attitudes, perceived behavioral control, and subjective norms are stronger in mixed-governance groups. The results of this research can provide recommendations for policymaking that may help to ensure the stable development of AIUs and rural development. [ABSTRACT FROM AUTHOR]