Zabuye Salt Lake solar pond in Tibet, China: Construction and operational experience

We describe the construction of the Zabuye Salt Lake solar pond and our experience during its operation. The salinity gradient was experimentally determined in the pond, which has a surface area of about 3588 m2, and different conditions and modes of operation. The method for establishing a salinity and temperature gradient can save large amounts of fresh water during the establishment of a temperature and salinity gradient in a solar pond. A technology to control solar pond operation was developed on the basis of our experimental results and is now being used to operate the pond

Regional differences of physical fitness and overweight and obesity prevalence among college students before and after COVID-19 pandemic since the “double first-class” initiative in China

IntroductionPhysical fitness has been widely recognized as a powerful marker of health in children and adolescents, and it negatively affected by the COVID-19 pandemic. The construction of world-class universities and first-class disciplines, known as the “Double First-Class” Initiative (DFC), is a major commitment made by the Chinese government to adapt to changes in the educational environment, both domestically and internationally, in order to promote the development and practice of international higher education. The aim of the study was to look deep into the regional differences of physical fitness and overweight and obesity prevalence among college students before and after the COVID-19 pandemic since the DFC.MethodsThe original physical fitness parameters of students from 10 DFC universities and colleges in Central South China were downloaded from the official website of Chinese National Student Physical Fitness Database (CNSPFD) and then divided into 3 groups based on the pandemic periods: pre-pandemic (2019), the first year after pandemic outbreak (2020), and the second year after pandemic outbreak (2021). All the data were stored in Excel 2010, analyzed by SPSS 17.0, and plotted with ArcGIS 10.4.ResultsThe total “fail” percentage (from 9.19% in 2019 to 12.94% in 2021) and the prevalence of overweight and obesity in boys (from 22.53 to 29.25% in 2021) exhibited a continuous increase year by year, and among all the physical fitness indicators the score of strength in boys and endurance quality in all individuals were the lowest in overweight and obesity groups. Students with ‘fail’ rate developed from northern and northeastern province to southern areas from 2019 to 2021. For grade 2019th, overweight and obesity students who also failed the test had covered nationwide and the most affected areas including northeast, east, as well as central north in senior year. The distribution of overall fitness assessments in Hubei province was in accordance with the national data, and the overall scoring growths in both class of 2021st and 2022nd were measured with a negative increase (p < 0.01).ConclusionThe government and related functional departments should take into consideration the student regional sources, especially in western and northeast regions of China, and school polices and physical education (PE) teachers should pay more attention to put training efforts on endurance for all adolescents and strength for boys and the group of overweight and obesity who also failed in the standard test, when designing specific interventions to promote physical health and counteract the negative effects of COVID-19 pandemic in college students

YOLOC-tiny: a generalized lightweight real-time detection model for multiripeness fruits of large non-green-ripe citrus in unstructured environments

This study addresses the challenges of low detection precision and limited generalization across various ripeness levels and varieties for large non-green-ripe citrus fruits in complex scenarios. We present a high-precision and lightweight model, YOLOC-tiny, built upon YOLOv7, which utilizes EfficientNet-B0 as the feature extraction backbone network. To augment sensing capabilities and improve detection accuracy, we embed a spatial and channel composite attention mechanism, the convolutional block attention module (CBAM), into the head’s efficient aggregation network. Additionally, we introduce an adaptive and complete intersection over union regression loss function, designed by integrating the phenotypic features of large non-green-ripe citrus, to mitigate the impact of data noise and efficiently calculate detection loss. Finally, a layer-based adaptive magnitude pruning strategy is employed to further eliminate redundant connections and parameters in the model. Targeting three types of citrus widely planted in Sichuan Province—navel orange, Ehime Jelly orange, and Harumi tangerine—YOLOC-tiny achieves an impressive mean average precision (mAP) of 83.0%, surpassing most other state-of-the-art (SOTA) detectors in the same class. Compared with YOLOv7 and YOLOv8x, its mAP improved by 1.7% and 1.9%, respectively, with a parameter count of only 4.2M. In picking robot deployment applications, YOLOC-tiny attains an accuracy of 92.8% at a rate of 59 frames per second. This study provides a theoretical foundation and technical reference for upgrading and optimizing low-computing-power ground-based robots, such as those used for fruit picking and orchard inspection

ASFL-YOLOX: an adaptive spatial feature fusion and lightweight detection method for insect pests of the Papilionidae family

IntroductionInsect pests from the family Papilionidae (IPPs) are a seasonal threat to citrus orchards, causing damage to young leaves, affecting canopy formation and fruiting. Existing pest detection models used by orchard plant protection equipment lack a balance between inference speed and accuracy.MethodsTo address this issue, we propose an adaptive spatial feature fusion and lightweight detection model for IPPs, called ASFL-YOLOX. Our model includes several optimizations, such as the use of the Tanh-Softplus activation function, integration of the efficient channel attention mechanism, adoption of the adaptive spatial feature fusion module, and implementation of the soft Dlou non-maximum suppression algorithm. We also propose a structured pruning curation technique to eliminate unnecessary connections and network parameters.ResultsExperimental results demonstrate that ASFL-YOLOX outperforms previous models in terms of inference speed and accuracy. Our model shows an increase in inference speed by 29 FPS compared to YOLOv7-x, a higher mAP of approximately 10% than YOLOv7-tiny, and a faster inference frame rate on embedded platforms compared to SSD300 and Faster R-CNN. We compressed the model parameters of ASFL-YOLOX by 88.97%, reducing the number of floating point operations per second from 141.90G to 30.87G while achieving an mAP higher than 95%.DiscussionOur model can accurately and quickly detect fruit tree pest stress in unstructured orchards and is suitable for transplantation to embedded systems. This can provide technical support for pest identification and localization systems for orchard plant protection equipment

Non-destructive detection of kiwifruit soluble solid content based on hyperspectral and fluorescence spectral imaging

The soluble solid content (SSC) is one of the important parameters depicting the quality, maturity and taste of fruits. This study explored hyperspectral imaging (HSI) and fluorescence spectral imaging (FSI) techniques, as well as suitable chemometric techniques to predict the SSC in kiwifruit. 90 kiwifruit samples were divided into 70 calibration sets and 20 prediction sets. The hyperspectral images of samples in the spectral range of 387 nm~1034 nm and the fluorescence spectral images in the spectral range of 400 nm~1000 nm were collected, and their regions of interest were extracted. Six spectral pre-processing techniques were used to pre-process the two spectral data, and the best pre-processing method was selected after comparing it with the predicted results. Then, five primary and three secondary feature extraction algorithms were used to extract feature variables from the pre-processed spectral data. Subsequently, three regression prediction models, i.e., the extreme learning machines (ELM), the partial least squares regression (PLSR) and the particle swarm optimization - least square support vector machine (PSO-LSSVM), were established. The prediction results were analyzed and compared further. MASS-Boss-ELM, based on fluorescence spectral imaging technique, exhibited the best prediction performance for the kiwifruit SSC, with the Rp2, Rc2 and RPD of 0.8894, 0.9429 and 2.88, respectively. MASS-Boss-PLSR based on the hyperspectral imaging technique showed a slightly lower prediction performance, with the Rp2, Rc2, and RPD of 0.8717, 0.8747, and 2.89, respectively. The outcome presents that the two spectral imaging techniques are suitable for the non-destructive prediction of fruit quality. Among them, the FSI technology illustrates better prediction, providing technical support for the non-destructive detection of intrinsic fruit quality