Combustion Noise Analysis for Combustion and Fuels Diagnosis of a CI Diesel Engine Operating with Biodiesels

In this paper, the combustion noise of a compression ignition (CI) diesel engine operating with biodiesels has been investigated experimentally. It aims to explore an effective method for combustion process monitoring and fuel quality evaluation through analysing the characteristics of the engine combustion noise. The experiments were conducted on a four-cylinder, four-stroke, direct injection and turbocharged diesel engine fuelled with biodiesels (B50 and B100) and normal pure diesel, and operating under different loads and speeds. The signals of cylinder head vibration, engine noise and in-cylinder pressure were measured during the tests. A coherent power spectrum analysis method was used to investigate the vibration and noise signals that related to the combustion process. The results shown that the noise components at the frequency band of 2 -3 kHz are closely related to the combustion process. Subsequently, the Wigner-Ville distribution is employed to present the energy distribution of engine noise in the time-frequency domain. Then a band-pass filter based on fractional Fourier transform (FRFT) is developed to extract the main component of the combustion noise for feature extraction. The results show that the sound pressure levels (SPLs) of the extracted combustion noise of the test diesel engine fuelled with biodiesels are higher than that fuelled with diesel. This is also identical to the variation of in-cylinder pressure. The results demonstrate that the features of the extracted combustion noise can indicate the combustion characteristics and provide useful information for monitoring the combustion process and evaluating the fuel quality of diesel engines

Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides

Objectives: To develop and validate a deep learning (DL)-based primary tumor biopsy signature for predicting axillary lymph node (ALN) metastasis preoperatively in early breast cancer (EBC) patients with clinically negative ALN. Methods: A total of 1,058 EBC patients with pathologically confirmed ALN status were enrolled from May 2010 to August 2020. A DL core-needle biopsy (DL-CNB) model was built on the attention-based multiple instance-learning (AMIL) framework to predict ALN status utilizing the DL features, which were extracted from the cancer areas of digitized whole-slide images (WSIs) of breast CNB specimens annotated by two pathologists. Accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curves, and areas under the ROC curve (AUCs) were analyzed to evaluate our model. Results: The best-performing DL-CNB model with VGG16_BN as the feature extractor achieved an AUC of 0.816 (95% confidence interval (CI): 0.758, 0.865) in predicting positive ALN metastasis in the independent test cohort. Furthermore, our model incorporating the clinical data, which was called DL-CNB+C, yielded the best accuracy of 0.831 (95%CI: 0.775, 0.878), especially for patients younger than 50 years (AUC: 0.918, 95%CI: 0.825, 0.971). The interpretation of DL-CNB model showed that the top signatures most predictive of ALN metastasis were characterized by the nucleus features including density ($p$ = 0.015), circumference ($p$ = 0.009), circularity ($p$ = 0.010), and orientation ($p$ = 0.012). Conclusion: Our study provides a novel DL-based biomarker on primary tumor CNB slides to predict the metastatic status of ALN preoperatively for patients with EBC. The codes and dataset are available at https://github.com/bupt-ai-cz/BALNMPComment: Update Table 1 and corresponding description

Breast Cancer Immunohistochemical Image Generation: a Benchmark Dataset and Challenge Review

For invasive breast cancer, immunohistochemical (IHC) techniques are often used to detect the expression level of human epidermal growth factor receptor-2 (HER2) in breast tissue to formulate a precise treatment plan. From the perspective of saving manpower, material and time costs, directly generating IHC-stained images from hematoxylin and eosin (H&E) stained images is a valuable research direction. Therefore, we held the breast cancer immunohistochemical image generation challenge, aiming to explore novel ideas of deep learning technology in pathological image generation and promote research in this field. The challenge provided registered H&E and IHC-stained image pairs, and participants were required to use these images to train a model that can directly generate IHC-stained images from corresponding H&E-stained images. We selected and reviewed the five highest-ranking methods based on their PSNR and SSIM metrics, while also providing overviews of the corresponding pipelines and implementations. In this paper, we further analyze the current limitations in the field of breast cancer immunohistochemical image generation and forecast the future development of this field. We hope that the released dataset and the challenge will inspire more scholars to jointly study higher-quality IHC-stained image generation.Comment: 13 pages, 11 figures, 2table

Syntectonic emplacement of Late Cretaceous mafic dyke swarms in coastal southeastern China: Insights from magnetic fabrics, rock magnetism and field evidence

Magma flow directions for 6 Late Cretaceous mafic dyke swarms exposed in coastal southeastern China (SE China) were analyzed using anisotropy of magnetic susceptibility (AMS) and field evidence. Normal AMS fabrics are predominant. The AMS of the dyke swarms originates mainly from the distribution anisotropy of intersertal magnetite that crystallized during late stage magma flow or after the magma cooled. The AMS fabrics record tectonic stress combined with magma flow. Sub-vertical to vertical magma flow is inferred from symmetrical imbricated magnetic foliations of dyke walls and field evidence for 5 dyke swarms. The inferred (sub-) vertical flow directions also indicate that the magma chambers were probably just beneath the sampled locations. Low anisotropy degree, different orientations of principal AMS axes, and asymmetrical magnetic foliations of normal fabrics oblique to dyke walls indicate syntectonic emplacement of the Late Cretaceous dyke swarms under an extensional tectonic regime caused by Paleo-Pacific plate subduction.This work was financially supported by Zhejiang Provincial Natural Science Foundation of China (Grant LY12D02002). Xiaoqing Pan is further supported by international cooperation and exchange project for doctoral candidates funded by Zhejiang University (Grant 188310- 540615/001)

Research of Optimal Arrangement of Sensor for Wind Turbine Gearbox based on Immune Algorithm

The signal acquisition is a prerequisite for fault analysis and diagnosis,but also the necessary condition in the fault diagnosis of wind turbine gearbox. At present,the most mature method is vibration signal analysis method,however,because the internal structure of the gearbox can not be destroyed,it can only lead to vibration sensor installed on the external wall of the gearbox. The sensors arrangement of gearbox will directly influence the quality of diagnostic accuracy. By using the immune algorithm to solve the problem of sensor arrangement in fault diagnosis of wind turbine,the gearbox vibration source coordinates is constructed,the optimal acquisition path of vibration transmission is calculated,the best measuring point is determined. The placement optimization of the measuring point for sensor is realized,an effective method for sensor arrangement is provided