Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Basic Disciplines of Agriculture submitted time 2023-08-14 Cooperative journals: 《智慧农业（中英文）》
Abstract： [Objective] Conducting research on the recognition of yak meat parts can help avoid confusion and substandard parts during the production and sales of yak meat, improve the transparency and traceability of the yak meat industry, and ensure food safety. To achieve fast and accurate recognition of different parts of yak meat, this study proposed an improved residual network model and developed a smartphone based yak meat part recognition software. [Methods] Firstly, the original data set of 1960 yak tenderloin, high rib, shank and brisket were expanded by 8 different data enhancement methods, including horizontal flip, vertical flip, random direction rotation 30°, random direction rotation 120°, random direction rotation 300° , contrast adjustment, saturation adjustment and hue adjustment. After expansion, 17,640 yak meat images of different parts were obtained. The expanded yak meat images of different parts were divided according to the 4:1 ratio, resulting in 14,112 yak meat sample images in the training set and 3528 yak meat sample images in the test set. Secondly, the convolutional block attention module (CBAM) was integrated into each residual block of the original network model to enhance the extraction of key detail features of yak images in different parts. At the same time, introducing this mechanism into the network model could achieve greater accuracy improvement with less computational overhead and fewer parameters. In addition, in the original network model, the full connection layer was directly added after all residual blocks instead of global average pooling and global maximum pooling, which could improve the accuracy of the network model, prevent overfitting, reduce the number of connections in subsequent network layers, accelerate the execution speed of the network model, and reduce the computing time when the mobile phone recognized images. Thirdly, different learning rates, weight attenuation coefficients and optimizers were used to verify the influence of the improved ResNet18_CBAM network model on convergence speed and accuracy. According to the experiments, the stochastic gradient descent (SGD) algorithm was adopted as the optimizer, and when the learning rate was 0.001 and the weight attenuation coefficient was 0, the improved ReaNet18_CBAM network model had the fastest convergence speed and the highest recognition accuracy on different parts of yak data sets. Finally, the PyTorch Mobile module in PyTorch deep learning framework was used to convert the trained ResNet18_CBAM network model into TorchScript model and saved it in *.ptl. Then, the yak part recognition App was developed using the Android Studio development environment, which included two parts: Front-end interface and back-end processing. The frontend of the App uses *.xml for a variety of price control layout, and the back-end used Java language development. Then TorchScript model in *.ptl was used to identify different parts of yak meat. [Results and Discussions] In this study, CBAM, SENet, NAM and SKNet, four popular attentional mechanism modules, were integrated into the original ResNet18 network model and compared by ablation experiments. Their recognition accuracy on different parts of yak meat dataset were 96.31%, 94.12%, 92.51% and 93.85%, respectively. The results showed that among CBAM, SENet, NAM and SKNet, the recognition accuracy of ResNet18 CBAM network model was significantly higher than that of the other three attention mechanism modules. Therefore, the CBAM attention mechanism module was chosen as the improvement module of the original network model. The accuracy of the improved ResNet18_CBAM network model in the test set of 4 different parts of yak tenderloin, high rib, shank and brisket was 96.31%, which was 2.88% higher than the original network model. The recognition accuracy of the improved ResNet18_CBAM network model was compared with AlexNet, VGG11, ResNet34 and ResNet18 network models on different parts of yak test set. The improved ResNet18_CBAM network model had the highest accuracy. In order to verify the actual results of the improved ResNet18_CBAM network model on mobile phones, the test conducted in Xining beef and mutton wholesale market. In the actual scenario testing on the mobile end, a total of 54, 59, 51, and 57 yak tenderloin, high rib, shank and brisket samples were collected, respectively. The number of correctly identified samples and the number of incorrectly identified samples were counted respectively. Finally, the recognition accuracy of tenderloin, high rib, shank and brisket of yak reached 96.30%, 94.92%, 98.04% and 96.49%, respectively. The results showed that the improved ResNet18_CBAM network model could be used in practical applications for identifying different parts of yak meat and has achieved good results. [Conclusions] The research results can help ensure the food quality and safety of the yak industry, improve the quality and safety level of the yak industry, improve the yak trade efficiency, reduce the cost, and provide technical support for the intelligent development of the yak industry in the Qinghai-Tibet Plateau region.
Subjects: Psychology >> Social Psychology submitted time 2023-03-27 Cooperative journals: 《心理学报》
Abstract： Depressed students are at high-risk for suicide. Psychological pain, especially pain avoidance, was a more robust predictor for suicide ideation than depression at the behavioral level. Due to suicide as a complex classification model, machine learning algorisms applied to integrate behavioral data and neural characteristic can advance suicide prediction, and the accuracy of multimodality features is superior than clinical interview. The present study aimed to integrate data-driven machine learning algorisms and the three-dimensional psychological pain model to figure out the optimal features in the prediction of suicide ideation. Seventy-seven college students were recruited by advertisement and divided into three groups: depressed group with high levels of suicide ideation (HSI, n = 25), depressed group with low levels of suicide ideation (LSI, n = 20), and healthy controls (HC, n = 32). All participants completed the three-dimensional psychological pain scale (TDPPS), Beck depression inventory-I (BDI), Beck suicide ideation inventory (BSI), and the self-referential affective incentive delay task (SAID). The value of support vector based on machine-recursive feature elimination (RFE-SVM) algorithm applied to combine the scale scores, resting state and punitive-related EEG components for feature ranking in a nonlinear way. Results showed that: (1) Scores of pain avoidance in the HSI was higher than the LSI group. (2) The multimodal psychological pain-based model for suicide ideation classification (Accuracy = 85.66%, Precision = 0.82, Recall = 0.73, AUC = 0.92) was sufficient and superior than the EEG single-modal model. Importantly, the pain avoidance and BDI scores ranked the top two features in the classification model of suicide ideation, whereas painful feeling and pain arousal subscale scores ranked the top two features in the classification model of depression. The EEG optimal features of overlap in the pain avoidance and suicide ideation classification models were the LPP and target-P3 under self-referential punitive conditions. (3) The powers of delta and beta band were negatively correlated with the BSI-W and pain avoidance subscale scores. The FRN amplitude under other- and self-referential punitive conditions were negatively corelated with the pain avoidance subscale scores. In the HSI group, power of delta elicited by positive feedback under self-referential conditions was significantly lower than those under other-referential conditions. In the HSI group, the amplitude of LPP in other-referential punitive conditions was higher than those under reward and neutral conditions, whereas in the LSI group, the amplitude of LPP under self-referential punitive conditions was higher than that under neutral conditions. As a pilot study, the current study provided a support for the prominent role of pain avoidance and its related neuroelectrophysiological correlates in the prediction of suicide. The clinical significance of these results will be discussed.
Subjects: Biology >> Ecology submitted time 2018-06-09 Cooperative journals: 《生态学报》
Abstract：在我国大力推动城市可持续发展，推进国家可持续发展实验区建设的同时，采用何种评估方法和数据开展城市可持续发展能力评估是需要重点解决的问题。近年来兴起的元数据理论与技术在解决评估数据质量控制方面被视为是一种行之有效的方法。针对我国现阶段使用的一些城市可持续发展能力评估指标体系的特点，通过深入剖析每一个指标数据的来源、获取手段、适用方法等特征，提出从软件工程学思路研发城市可持续发展能力评估元数据管理系统的具体方法，帮助可持续发展实验区高效获取和管理评估所需数据信息；以"十二五"科技支撑计划项目"城市可持续发展能力评估及信息管理关键技术研究与示范"中所建立的元数据规范，对其所包含的"数据发布日期"、"数据发布形式"、"空间范围"、"时间范围（起始时间、结束时间）"、"统计频率"、"数据安全限制分级"、"数据志说明"、"在线资源链接地址"和"数据统计单位信息（单位名称、联络人、联系电话、单位地址、邮件地址）"共14项为评估数据的关键元数据项，以此追踪对标的评估数据。并通过量化数据质量评分法针对数据质量在运用元数据追踪法前后的评价结果对比发现，被评估指标的数据质量在获得元数据支持时，其数据可靠性、可比性和可持续性方面的评价分值都获得了十分显著的改善。研究认为采用元数据理论在控制和保障城市可持续发展能力评估数据质量方面具有优势作用，开发有针对性的城市可持续发展能力评估元数据管理系统能够有效提高评估数据的综合评价结果。
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