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S-N curve fatigue samples of titanium alloy welded joints have such a comparatively significant scatter, that results in the issue that the fatigue life prediction accuracy is not optimal. In this work, the titanium alloy welded joints' fatigue data is used as analysis data, and the neighborhood rough set reduction with improved firefly algorithm efficient method of fitting stress-life curves is set forth. The welded joint's fatigue decision system is built with fatigue data. The continuous iteration of the firefly algorithm is used as the search strategy, the neighborhood rough set is adopted to decrease attributes, and the major deciding elements of welded joints' fatigue life is identified. The fatigue characteristic domains are divided based on the neighborhood rough set reduction with improved firefly algorithm's key factor set, and the S-N curves can then be fitted to each domain individually. According to the goodness-of-fit analysis, the proposed approach can improve fatigue life accuracy and reduce sample scattering from fatigue.

Multiple instance learning (MIL) has emerged as a popular method for classifying histopathology whole slide images (WSIs). However, existing approaches typically rely on pre-trained models from large natural image datasets, such as ImageNet, to generate instance features, which can be sub-optimal due to the significant differences between natural images and histopathology images that lead to a domain shift. In this paper, we present a novel, simple yet effective method for learning domain-specific knowledge transformation from pre-trained models to histopathology images. Our approach entails using a prompt component to assist the pre-trained model in discerning differences between the pre-trained dataset and the target histopathology dataset, resulting in improved performance of MIL models. We validate our method on two publicly available datasets, Camelyon16 and TCGA-NSCLC. Extensive experimental results demonstrate the significant performance improvement of our method for different MIL models and backbones. Upon publication of this paper, we will release the source code for our method., Comment: Submitted to MICCAI 2023

Scientific and reasonable examination mode can not only make effective evaluation and feedback, but also play an important role in cultivating students' good learning habits and shaping their social and vocational adaptability. Meanwhile, it is also an important method of cultivating students' innovative spirit and creative ability. Specialty direction on tobacco emphasizes cultivating senior disciplinary talents who will engage in tobacco promotion and rational use, tobacco marketing and tobacco enterprise management. So it is necessary to practice various and effective examination modes to make students master the knowledge they have learned well and truly achieve the aim of professional training. Thus the students may become qualified talents with professional quality of tobacco industry.

Carbon isotopes(δ13C) of carbonate from sediment core has been measured in Lake Caohai,Guizhou Province,southwest China.The results showed that δ13C values varied between-14.25‰ and 23.10‰,which was the largest amplitude of carbon isotope variations and the highest positive values discovered so far in lacusrine carbonate sediments.On the basis of combination with oxygen isotope,organic matter and carbonate content proxies,we suggested that the carbon isotope of carbonate from Lake Caohai was controlled by photosynthesis/respiration of aquatic plants,and the input of bacteria to carbon isotope fractionation of organic matter might be responsible for the abnormal positive δ13C values in Lake Caohai.Therefore,much attention should be paid to the carbonate δ13C in Lake Caohai in the future research.

The research of stable carbon and oxygen isotope of Carbonate of lake sediments, is one of the most effective indices to reconstruct paleoclimatic and paleoenvironmental information. Sedimentary carbonate consists mainly of allochthonous carbonates produced by surface runoff and authigenic carbonates produced by inorganic precipitation. However, the authigenic carbonate has the paleoclimatic and paleoenvironmental meaning. Consequently, distinguishing the source of carbonate is the basis and premise for the research of sedimentary carbonates. This paper utilized various methods to prove that the carbonate in Lake Chenghai,Yunnan Province was authigenic, which provided the basis for the research of sedimentary carbonates. Lake Chenghai provides an ideal lacustrine setting for research of stable carbon and oxygen isotope to reconstruct paleoclimate, and the future research will be deserved.