Image Retrieval Method Combining Bayes and SVM Classifier Based on Relevance Feedback with Application to Small-scale Datasets

A vast amount of images has been generated due to the diversity and digitalization of devices for image acquisition. However, the gap between low-level visual features and high-level semantic representations has been a major concern that hinders retrieval accuracy. A retrieval method based on the transfer learning model and the relevance feedback technique was formulated in this study to optimize the dynamic trade-off between the structural complexity and retrieval performance of the small- and medium-scale content-based image retrieval (CBIR) system. First, the pretrained deep learning model was fine-tuned to extract features from target datasets. Then, the target dataset was clustered into the relative and irrelative image library by exploring the Bayes classifier. Next, the support vector machine (SVM) classifier was used to retrieve similar images in the relative library. Finally, the relevance feedback technique was employed to update the parameters of both classifiers iteratively until the request for the retrieval was met. Results demonstrate that the proposed method achieves 95.87% in classification index F1 - Score, which surpasses that of the suboptimal approach DCNN-BSVM by 6.76%. The performance of the proposed method is superior to that of other approaches considering retrieval criteria as average precision, average recall, and mean average precision. The study indicates that the Bayes + SVM combined classifier accomplishes the optimal quantities more efficiently than only either Bayes or SVM classifier under the transfer learning framework. Transfer learning skillfully excels training from scratch considering the feature extraction modes. This study provides a certain reference for other insights on applications of small- and medium-scale CBIR systems with inadequate samples

Long-term effects of restoration on the links between above-and belowground biodiversity in degraded Horqin sandy grassland, Northern China

Long-term ecological restoration plays an important role in the sustainable development of degraded grassland ecosystem. In this study, the levels of species diversity, genetic diversity and soil microbial diversity in restored grassland were measured by vegetation survey, DNA barcoding and soil microbial high-throughput sequencing technology, so as to explore the relationship between above- and belowground biodiversity and its driving factors in Horqin sandy grassland. In this study, the results found that herb are dominated in restoration grassland types. Plant species richness (SR) from post-non-grazing restoration plot (NGR) communities was significantly higher than other restoration communities (10 ± 1.1, p = 0.004). Genetic diversity indices of dominant plant species in chloroplast DNA (cpDNA), were remarkable greater than nuclear DNA (nrDNA) in each recovering sandy grassland plots (amplitude of difference was 44.8%–70.5% in allelic richness (AR), 81.9%–128.1% in expected heterozygosity (HE)). The soil bacterial and fungal richness from natural mobile dune grassland (NM) communities was notably lower than that from recovering grassland types (1641.9 ± 100.4, p < 0.001; 533 ± 16.6, p < 0.001). In this study, heterogeneous levels of genetic variability among different recovering sandy grassland types were detected. Correlation analyses revealed that there were positive correlations between species diversity and genetic diversity (SR & AR: r = 0.56, R2 = 0.31, p < 0.001; SR & HE: r = 0.33, R2 = 0.11, p = 0.045) and a negative correlation between soil microbial diversity and genetic diversity (r = -0.44, R2 = 0.19, p = 0.005). The final structural equation model explained 38% of the variance in SR, 57% in AR, 52% in soil microbial diversity (SD), 49% in aboveground biomass (AGB), 87% in soil organic carbon (SOC), 47% in soil alkali-hydrolyzable nitrogen (SAN) and 69% in soil available phosphorus (SOP). Long-term ecological restoration had significant direct positive effects on AGB, SOC, SAN, SOP, AR, SR and SD. There was a negative correlation between above- and belowground biodiversity and biological and abiotic factors. The results of this study have clarified the above- and underground biodiversity levels of sandy grassland and the relationship with driving factors under long-term ecological restoration measures, and will provide effective support for the management and sustainable development of sandy grassland

An Obligate Role of Oxytocin Neurons in Diet Induced Energy Expenditure

Oxytocin neurons represent one of the major subsets of neurons in the paraventricular hypothalamus (PVH), a critical brain region for energy homeostasis. Despite substantial evidence supporting a role of oxytocin in body weight regulation, it remains controversial whether oxytocin neurons directly regulate body weight homeostasis, feeding or energy expenditure. Pharmacologic doses of oxytocin suppress feeding through a proposed melanocortin responsive projection from the PVH to the hindbrain. In contrast, deficiency in oxytocin or its receptor leads to reduced energy expenditure without feeding abnormalities. To test the physiological function of oxytocin neurons, we specifically ablated oxytocin neurons in adult mice. Our results show that oxytocin neuron ablation in adult animals has no effect on body weight, food intake or energy expenditure on a regular diet. Interestingly, male mice lacking oxytocin neurons are more sensitive to high fat diet-induced obesity due solely to reduced energy expenditure. In addition, despite a normal food intake, these mice exhibit a blunted food intake response to leptin administration. Thus, our study suggests that oxytocin neurons are required to resist the obesity associated with a high fat diet; but their role in feeding is permissive and can be compensated for by redundant pathways

Calibration method of camera parameters for coal level measurement in coal bi

A calibration method of camera parameters based on planar grid was proposed to deal with the problem of intrinsic and extrinsic parameters calibration for photoelectric measuring device in coal bin. Two values of black and white are used to divide pattern on the calibration plate, and the intersection points of black and white values are taken as calibration points. Imaging postures with different perspectives are considered to obtain position information as many as possible. According to rotation invariance of angle feature, Harris algorithm is used to extract angular points, and nonlinear optimization method is adopted to calibrate parameters. The industrial tests are taken in coal bin of Daliuta coal mine of Shendong Coal Group. The test results show that the method can achieve better calibration results of coal level, and error is accepted, which meets needs of depth estimation of coal level in coal bin

Application of non line-of-sight error suppression method in mine target locatio

It was analyzed and pointed out that distance ranging error caused by non line-of-sight transmission delay of electromagnetic wave was the main reason of reducing mine location precision. For mine target location method based on time of arrival, a non line-of sight error suppression method based on energy detection was proposed. In the method, threshold of direct path and length of signal intercept time are analyzed and counted by testing channel characteristics of coal mine tunnel. In order to identify non line-of-sight signal, arrival time of direct path energy blocks is presented in extracted signal segment by constructing corresponding identification parameters. The experimental testing result shows the method achieves higher range and location precision

Early prediction of acute pancreatitis severity based on changes in pancreatic and peripancreatic computed tomography radiomics nomogram.

BACKGROUND: Early identification of severe acute pancreatitis (SAP) is key to reducing mortality and improving prognosis. We aimed to establish a radiomics model and nomogram for early prediction of acute pancreatitis (AP) severity based on contrast-enhanced computed tomography (CT) images. METHODS: We retrospectively analyzed 215 patients with first-episode AP, including 141 in the training cohort (87 men and 54 women, mean age 51.37±16.09 years) and 74 in the test cohort (40 men and 34 women, mean age 55.49±17.83 years). Radiomics features were extracted from portal venous phase images based on pancreatic and peripancreatic regions. The light gradient boosting machine (LightGBM) algorithm was used for feature selection, a logistic regression (LR) model was established and trained by 10-fold cross-validation, and a nomogram was established based on the best features. The models predictive performance was evaluated according to the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, sensitivity, specificity, and accuracy. RESULTS: A total of 13 optimal radiomics features were selected by LightGBM for LR model building. The AUC of the radiomics (LR) model was 0.992 [95% confidence interval (CI): 0.963-0.996] in the training cohort, 0.965 (95% CI: 0.924-0.981) in the validation cohort, and 0.894 (95% CI: 0.789-0.966) in the test cohort. The sensitivity was 0.862 (95% CI: 0.674-0.954), the specificity was 0.800 (95% CI: 0.649-0.899), and the accuracy was 0.824 (95% CI: 0.720-0.919). The nomogram based on the 13 radiomics features showed that SAP would be predicted when the total score was greater than 124. CONCLUSIONS: The radiomics model based on enhanced-CT images of pancreatic and peripancreatic regions performed well in the early prediction of AP severity. The nomogram based on selected radiomics features could provide a reference for AP clinical assessment