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The climate and ecological problems caused by excessive carbon dioxide emissions are attracting more and more attention, and the need for carbon reduction has reached a consensus. Carbon peak and carbon neutrality is a solemn commitment made by China to the world and a strategic arrangement to achieve economic and social transformation. This study focused on Shanxi Province, a base of power source and heavy chemical industry in China. Based on energy consumption data, carbon emission data set at the county level, land use data and socioeconomic data, we built a carbon ecological pressure index in order to analyze the spatiotemporal evolution characteristics of the carbon footprint, carbon carrying capacity and carbon ecological security status of each county in Shanxi Province from 2000 to 2020. The results showed that: (1) the total carbon footprint of Shanxi Province increased, and the number of high carbon emission counties showed an increasing trend. The largest part of carbon footprint was coal. (2) The vegetation carbon-carrying capacity showed an increasing trend in general, and forest land was the main contributor to the carbon-carrying capacity. (3) The carbon deficit of Shanxi Province was greater than 0 and behaved as a carbon source. The carbon ecological security decreased from a relatively safe level to a general safe level. (4) The carbon ecological pressure index gradually increased. It was predicted that the carbon ecological security level of each county will remain basically unchanged by 2025 and some will still be at a carbon ecological insecurity level. In general, the carbon ecological pressure index of some counties was still large. It is necessary to strengthen the use of clean energy, optimize the industrial structure and increase the carbon sink of forest land in order to reduce carbon emissions and increase the carbon sink, so as to ensure carbon ecological security and realize the goal of a low-carbon economy.

Xiaojing Bai,1,2,* Yumei Jiang,1,2,* Xinwei Zhang,1,2 Meiyu Wang,3 Juanhua Tian,1 Lijun Mu,1 Yuefeng Du1,2 1Department of Urology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi 710061, People’s Republic of China; 2Oncology Research Lab, Key Laboratory of Environment and Genes Related to Diseases, Ministry of Education, Xi’an, Shaanxi 710061, People’s Republic of China; 3Department of Imaging, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi 710061, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yuefeng DuDepartment of Urology, The First Affiliated Hospital of Xi’an Jiaotong University, 277 Western Yanta Road, Xi’an, Shaanxi 710061, People’s Republic of ChinaTel/ Fax +86-29-8532-3945Email duyuefeng_123@163.comObjective: The aim of this study was to explore the value of the prostate-specific antigen (PSA) levels, the ratio of free PSA to total PSA (fPSA/TPSA), the PSA density (PSAD), digital rectal examination (DRE), transrectal prostate ultrasound (TRUS), and multiparameter MRI (MP-MRI) in the differential diagnosis of benign prostatic hyperplasia (BPH) and prostate cancer (PCa).Methods: From February 2016 to September 2019, data from 620 patients who underwent systematic transrectal ultrasound-guided prostate biopsy (STURS-PB) in our hospital were retrospectively collected, including the PSA levels, the fPSA/TPSA ratio, the PSAD, DRE, TRUS, MP-MRI, prostate volume, and other clinical data.Results: Among the 620 patients, 249 patients were in the PCa group, and 371 patients in the BPH group. The positive puncture rate was 40.16%. The positive predictive values of DRE, TRUS, mpMRI, and TPSA levels for PCa were 39.91%, 39.38%, 64.14%, and 41.57%, respectively; the sensitivity of these parameters was 37.35%, 51.41%, 74.69%, and 57.43%, respectively; and the specificity of these parameters was 62.26%, 46.90%, 71.97%, and 45.82%, respectively. When the TPSA concentration was in the range of 4– 20 ng/mL, the positive puncture rate of STURS-PB was 23.18%, with a high rate of misdiagnosis. When the TPSA concentration was in the range of 4– 20 ng/mL, the fPSA/TPSA ratio was 0.15, the PSAD was 0.16, the comprehensive evaluation of PCa was optimal (the sensitivity of these parameters was 88.85% and 84.09%, respectively; the specificity was 80.17% and 67.29%, respectively; the positive predictive value was 57.41% and 51.39%, respectively). When the TPSA concentration > 4 ng/mL, the fPSA/TPSA ratio ≤ 0.15 and the PSAD ≥ 0.16, the sensitivity, specificity, and correctness index of the PCa and BPH diagnosis were 80.54%, 82.75%, and 67.07%, respectively.Conclusion: When using DRE, TRUS, and MP-MRI to screen for PCa, MP-MRI has a relatively high sensitivity and specificity. Using these three thresholds (TPSA > 4 ng/mL combined with an fPSA/TPSA ratio ≤ 0.15 and a PSAD ≥ 0.16) is significantly better than using TPSA levels alone for the differential diagnosis of PCa and BPH.Keywords: total prostate-specific antigen, free prostate-specific antigen, prostate-specific antigen density, prostate cancer, benign prostatic hyperplasia

Reliable flame monitoring is essential to enhance the safety of industrial boilers. This paper presents a new self-diagnostic system to measure the oscillation frequency of a burner flame. The system incorporates three sensors including a microphone, a photodiode and an electrostatic electrode and simultaneously acquires three signals. The oscillation frequencies from the three sensors are determined through power spectral analysis, and a fused result of the three frequencies is obtained as the oscillation frequency of the burner flame. Moreover, detection and location of the system faults are realized using a self-diagnostic algorithm through the cross-correlation signal processing. Experimental tests were performed on a laboratory-scale combustion test rig with methane as the test fuel. The results demonstrate that the method is capable of measuring the oscillation frequency of a burner flame. In addition, the results are helpful for the comprehensive analysis of the oscillatory behaviors of burner flames. The self-diagnostic algorithm is able to detect the fault of the monitoring system and no additional self-diagnostic hardware is required.

Accurate and reliable segmentation of flame images are crucial in vision based monitoring and characterization of flames. It is, however, difficult to maintain the segmentation accuracy while achieving fast processing time due to the impact of the background noise in the images and the variation of operation conditions. To improve the quality of the image segmentation, a flame image segmentation method is proposed based on Multiscale Color and Wavelet-based Textures?MCWT? of the images. By combining the color and texture features, a characteristic matrix is built and then compressed using a local mean method. The outer contour of the flame image under the compressed scale is detected by a cluster technique. Subsequently, the flame edge region under the original scale is determined, following that, the characteristic matrix of the edge region is constructed and classified, and finally, the flame image segmentation is achieved. Flame images captured from an industrial-scale coal-firedtest rig under different operation conditions are segmented to evaluate the proposed method. The test results demonstrate that the performance of segmenting flame images of the proposed method is superior to other traditional methods. It also has been found that the proposed method performs more effectively in segmenting the flame images with Gaussian and pepper and salt noise.

The images of turbulent flame were acquired by using a digital imaging system on an industry-scale pulverized coal-fired test rig?The fractal dimensions of thin thermal mixing layers in flame were computed through morphology-based flame image processing techniques?The effects of the ratios of primary air and secondary/tertiary air on fractal dimensions were characterized?The results presented in this work show that the variations of fractal dimension are closely related to the ratio changes of primary air and secondary/tertiary air. Therefore?the fractal dimensions of flame thin thermal mixing layers can be used as an important indicator for the control and optimization of a combustion process.