The effects of heavy fuel oil and biodiesel blends on engine combustion and emissions were studied in a marine two-stroke diesel engine. The engine was operated under propeller conditions using five different fuels with biodiesel blends of 10% (B10), 30% (B30), 50% (B50), and sulphur contents of 0.467% low sulphur fuel oil (LSFO) and 2.9% high sulphur fuel oil (HSFO). Tests have shown that using a biodiesel blend increases the engine fuel consumption due to its lower calorific value. Heavy fuel oil has a high Polycyclic aromatic hydrocarbons (PAH) content, which leads to higher exhaust temperatures due to severe afterburning in the engine. A comparison of engine soot emissions under different fuel conditions was carried out, and it was found that the oxygen content in biodiesel promoted the oxidation of soot particles during the combustion process, which reduced the soot emissions of biodiesel. Compared to HSFO, B10, B30, B50 and LSFO, the soot emission concentrations were reduced by 50.2%, 56.4%, 61% and 37.4%, respectively. In our experiments, the soot particles in the engine exhaust were sampled with a thermal float probe. Using Raman spectroscopy analysis, it was found that as the biodiesel ratio increased, the degree of carbonisation of the soot particles in the exhaust became less than that in the oxygenation process, resulting in a decrease in the degree of graphitisation.

In medical applications, the diffusion of contrast agents in tissue can reflect the physiological function of organisms, so it is valuable to quantify the distribution and content of contrast agents in the body over a period. Spectral CT has the advantages of multi-energy projection acquisition and material decomposition, which can quantify K-edge contrast agents. However, multiple repetitive spectral CT scans can cause excessive radiation doses. Sparse-view scanning is commonly used to reduce dose and scan time, but its reconstructed images are usually accompanied by streaking artifacts, which leads to inaccurate quantification of the contrast agents. To solve this problem, an unsupervised sparse-view spectral CT reconstruction and material decomposition algorithm based on the multi-channel score-based generative model (SGM) is proposed in this paper. First, multi-energy images and tissue images are used as multi-channel input data for SGM training. Secondly, the organism is multiply scanned in sparse views, and the trained SGM is utilized to generate multi-energy images and tissue images driven by sparse-view projections. After that, a material decomposition algorithm using tissue images generated by SGM as prior images for solving contrast agent images is established. Finally, the distribution and content of the contrast agents are obtained. The comparison and evaluation of this method are given in this paper, and a series of mouse scanning experiments are carried out to verify the effectiveness of the method.

BACKGROUND: The rapid development of industrialization in printed circuit board (PCB) warrants more complexity and integrity, which entails an essential procedure of PCB inspection. X-ray computed laminography (CL) enables inspection of arbitrary regions for large-sized flat objects with high resolution. PCB inspection based on CL imaging is worthy of exploration. OBJECTIVE: This work aims to extract PCB circuit layer information based on CL imaging through image segmentation technique. METHODS: In this work, an effective and applicable segmentation model for PCB CL images is established for the first time. The model comprises two components, with one integrating edge diffusion and l ₀ smoothing to filter CL images with aliasing artifacts, and the other being the fuzzy energy-based active contour model driven by local pre-fitting energy to segment the filtered images. RESULT: The proposed model is able to suppress aliasing artifacts in the PCB CL images and has good performance on images of different circuit layers. CONCLUSIONS: Results of the simulation experiment reveal that the method is capable of accurate segmentation under ideal scanning condition. Testing of different PCBs and comparison of different segmentation methods authenticate the applicability and superiority of the model.