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Volatile components in red orange essential oil extracted by two cold pressing processes (cup pressing and lime method) were analyzed by gas chromatography quadrupole time-of-flight mass spectrometry (GC-Q/TOF). A total of 93 components were identified, 89 and 84 compounds were identified for the two types of red orange essential oils, respectively. Among the 93 components, there were 29 terpenes, 20 alcohols, 20 aldehydes, 5 esters, 2 ketones, 12 hydrocarbons, and 5 other compounds. There were 80 compounds shared by the two essential oils. The total volatile component content of red orange essential oil by cup pressing method was significantly higher than that by lime method. The highest content of these compounds was terpenes which total content was over 80%. In addition, the compounds with higher content were D-limonene, γ-terpinene, β-myrcene, linalool and α-pinene. According to principal component analysis (PCA), there was a significant difference in the volatile components of the two types of red orange essential oils. The relative odor activity value (ROAV) method indicated that linalool was the most important aroma component of the two red orange essential oils. And the citrus aroma properties of red orange essential oil extracted by cup pressing method was significantly stronger than that extracted by lime method. Linalool, thymol, β-myrcene, D-limonene, citronellal, and γ-terpinene were confirmed to the key aroma components of the red orange essential oils.

Nowadays, the rapid development of hypersonic vehicles brings great challenges to the missile defense system. As achieving successful interception depends highly on terminal guidance laws, research on guidance laws for intercepting highly maneuvering targets has aroused increasing attention. Artificial intelligence technologies, such as deep reinforcement learning (DRL), have been widely applied to improve the performance of guidance laws. However, the existing DRL guidance laws rarely consider the partial observability problem of onboard sensors, resulting in the limitations of their engineering applications. In this paper, a deep recurrent reinforcement learning (DRRL)-based guidance method is investigated to address the intercept guidance problem against maneuvering targets under partial observability. The sequence consisting of previous state observations is utilized as the input of the policy network. A recurrent layer is introduced into the networks to extract hidden information behind the temporal sequence to support policy training. The guidance problem is formulated as a partially observable Markov decision process model, and then a range-weighted reward function that considers the line-of-sight rate and energy consumption is designed to guarantee convergence of policy training. The effectiveness of the proposed DRRL guidance law is validated by extensive numerical simulations.

Feeding behavior is known to affect the welfare and fattening efficiency of yaks in feedlots. With the advancement of machine vision and sensor technologies, the monitoring of animal behavior is progressively shifting from manual observation towards automated and stress-free methodologies. In this study, a real-time detection model for individual yak feeding and picking behavior was developed using YOLO series model and StrongSORT tracking model. In this study, we used videos collected from 11 yaks raised in two pens to train the yak face classification with YOLO series models and tracked their individual behavior using the StrongSORT tracking model. The yak behavior patterns detected in trough range were defined as feeding and picking, and the overall detection performance of these two behavior patterns was described using indicators such as accuracy, precision, recall, and F1-score. The improved YOLOv8 and Strongsort model achieved the best performance, with detection accuracy, precision, recall, and F1-score of 98.76%, 98.77%, 98.68%, and 98.72%, respectively. Yaks which have similar facial features have a chance of being confused with one another. A few yaks were misidentified because their faces were obscured by another yak’s head or staff. The results showed that individual yak feeding behaviors can be accurately detected in real-time using the YOLO series and StrongSORT models, and this approach has the potential to be used for longer-term yak feeding monitoring. In the future, a dataset of yaks in various cultivate environments, group sizes, and lighting conditions will be included. Furthermore, the relationship between feeding time and yak weight gain will be investigated in order to predict livestock weight.

Abstract Pursuit of improved living quality has stimulated great demand for high-performance conformal healthcare devices in modern human society. However, manufacturing of efficient, comfortable and stretchable biomedical apparatus faces huge challenges using traditional materials. Liquid metals (LMs) show remarkable potential to solve this problem due to their extraordinary biocompatibility, stretchability, thermal and electrical conductivity. In recent years, tremendous explorations have attempted to make stretchable biomedical devices with LMs. Herein, we review the stretchable LM-based biomedical devices on the topics of disease treatment and human function augmenting. The representative and up-to-date neural interfaces, alloy cement, e-vessels, soft heaters, exoskeletons, and e-skins are summarized. The existing issues of LMs applied for biomedical devices are also discussed. This review can provide guidance for the follow-up research in LM-based biomedical devices.

Yak behavior is a valuable indicator of their welfare and health. Information about important statuses, including fattening, reproductive health, and diseases, can be reflected and monitored through several indicative behavior patterns. In this study, an improved YOLOv7-pose model was developed to detect six yak behavior patterns in real time using labeled yak key-point images. The model was trained using labeled key-point image data of six behavior patterns including walking, feeding, standing, lying, mounting, and eliminative behaviors collected from seventeen 18-month-old yaks for two weeks. There were another four YOLOv7-pose series models trained as comparison methods for yak behavior pattern detection. The improved YOLOv7-pose model achieved the best detection performance with precision, recall, mAP0.5, and mAP0.5:0.95 of 89.9%, 87.7%, 90.4%, and 76.7%, respectively. The limitation of this study is that the YOLOv7-pose model detected behaviors under complex conditions, such as scene variation, subtle leg postures, and different light conditions, with relatively lower precision, which impacts its detection performance. Future developments in yak behavior pattern detection will amplify the simple size of the dataset and will utilize data streams like optical and video streams for real-time yak monitoring. Additionally, the model will be deployed on edge computing devices for large-scale agricultural applications.

To improve the drying efficiency and quality of hot air-dried apple slices with blanching pretreatment, this study systematically evaluated the effects of two blanching pretreatments, hot water blanching and vacuum steam pulsed blanching, on the macroscopic drying performance of apple slices. The results indicated that under the conditions of 0.07 MPa vacuum, two blanching cycles, and blanching time of 3 minutes, vacuum steam pulsed blanching significantly enhanced the rehydration rate and textural quality of apple slices while reducing the drying time. Paraffin sectioning, microscopic observation and image processing were used to compare the microstructural changes of apple slices in terms of cell cross-sectional area, perimeter, equivalent diameter, cell roundness, wall roughness factor, and porosity under different branching conditions (temperature and time for hot water blanching; vacuum level, number of cycles, and time for vacuum steam pulsed blanching). The results showed that with the increase in temperature, blanching time and number of blanching cycles, apple cells exhibited changes that accelerated water evaporation during subsequent drying, thus shortening the drying time. Additionally, the relationship between microstructural parameters and rehydration ratio was fitted to a polynomial regression model. The findings of this study provide a theoretical basis for understanding the mechanisms by which vacuum steam pulsed blanching affects the microstructure and quality of dried apple slices.