A new characterization of fuzzy ideals of semigroups and its applications

In this paper, we develop a new technique for constructing fuzzy ideals of a semigroup. By using generalized Green\u27s relations, fuzzy star ideals are constructed. It is shown that the new fuzzy ideal of a semigroup can be used to investigate the relationship between fuzzy sets and abundance and regularity for an arbitrary semigroup. Appropriate examples of such fuzzy ideals are given in order to illustrate the technique. Finally, we explain when a semigroup satisfies conditions of regularity

Research on the Comparison and Development of Matchmaking Services at Home and Abroad

This article summarizes the feasibility of using value co-creation theory to operate and manage the internet matchmaking industry by reviewing the matchmaking service models and characteristics of developed countries and regions abroad, and combining it with the current development status of internet matchmaking websites in China. This provides reference suggestions for the construction of services for Chinese internet matchmaking enterprises and has practical significance for the current development of internet matchmaking services in China

Incorporating comorbidities into latent treatment pattern mining for clinical pathways

AbstractIn healthcare organizational settings, the design of a clinical pathway (CP) is challenging since patients following a particular pathway may have not only one single first-diagnosis but also several typical comorbidities, and thus it requires different disciplines involved to put together their partial knowledge about the overall pathway. Although many data mining techniques have been proposed to discover latent treatment information for CP analysis and reconstruction from a large volume of clinical data, they are specific to extract nontrivial information about the therapy and treatment of the first-diagnosis. The influence of comorbidities on adopting essential treatments is crucial for a pathway but has seldom been explored. This study proposes to extract latent treatment patterns that characterize essential treatments for both first-diagnosis and typical comorbidities from the execution data of a pathway. In particular, we propose a generative statistical model to extract underlying treatment patterns, unveil the latent associations between diagnosis labels (including both first-diagnosis and comorbidities) and treatments, and compute the contribution of comorbidities in these patterns. The proposed model extends latent Dirichlet allocation with an additional layer for diagnosis modeling. It first generates a set of latent treatment patterns from diagnosis labels, followed by sampling treatments from each pattern. We verify the effectiveness of the proposed model on a real clinical dataset containing 12,120 patient traces, which pertain to the unstable angina CP. Three treatment patterns are discovered from data, indicating latent correlations between comorbidities and treatments in the pathway. In addition, a possible medical application in terms of treatment recommendation is provided to illustrate the potential of the proposed model. Experimental results indicate that our approach can discover not only meaningful latent treatment patterns exhibiting comorbidity focus, but also implicit changes of treatments of first-diagnosis due to the incorporation of typical comorbidities potentially

Static Experimental Study on Flame Retardant and Explosion Suppression Performances of Fire Resistant Diesel Fuel

AbstractTo assess the flame retardant and explosion suppression performances of fire resistant diesel fuel, static experiments with ordinary diesel fuel (Diesel fuel 1, D1 for short) and fire resistant diesel fuel (Diesel fuel 2, D2 for short) detonated by explosives were performed in this study. The explosion process and surface temperature of the fireballs were recorded using a high-speed camera and an infrared thermal imager. Meanwhile, the overpressures of the explosion shock waves of the two diesels were also recorded using pressure sensors embedded in the ground. The experimental results show that the diesel fuels are dispersed and ignited to produce explosion fireball when explosive is detonated in fuel tank. At the same time, part of diesel fuel produces pool fire on the ground. The pool fire of D1 lasts about 3000ms, while D2 lasting only about 700ms. The maximum temperature and the duration of high temperature of D1 explosion fireball are 1558.8°C and 1392ms respectively, which are 1.11 and 1.29 times those of D2. In the position of 2 m far from the vertical projection point of the explosion center, the overpressure of the explosion shock wave of D1 is 53.30kPa, while that of D2 is 31.60kPa. Moreover, the overpressures of D1 are also higher in the other location of the pressure area. Therefore, it is proved that the explosive power of D2 is significantly lower than that of D1, and the flame retardant and explosion suppression performances of D2 is better than those of D1

Matrine inhibits hepatocellular carcinoma cell malignancy through the circ_0013290/miR-139-5p/MMP16 pathway

Background. Previous studies have shown the anticancer effect of Matrine on hepatocellular carcinoma (HCC); however, the underlying mechanism is still indistinct. Methods. The expression of circular RNA_0013290 (circ_0013290), microRNA-139-5p (miR-139-5p), matrix metallopeptidase 16 (MMP16), CyclinD1 and N-cadherin was analyzed by quantitative real-time polymerase chain reaction, Western blotting or immuno-histochemistry assay. Cell viability, proliferation, apoptosis, invasion and tube formation were analyzed by cell counting kit-8, 5-Ethynyl-2’-deoxyuridine, flow cytometry analysis, transwell invasion and tube formation assays, respectively. The associations among circ_0013290, miR-139-5p and MMP16 were predicted by starbase online database, and identified by dual-luciferase reporter and RNA pull-down assays. A xenograft mouse model assay was conducted to disclose the effects of circ_0013290 and Matrine on tumor tumorigenesis in vivo. Results. Circ_0013290 and MMP16 expression were significantly upregulated, while miR-139-5p was downregulated in HCC tissues and cells compared with the matched normal liver tissues and cells. Matrine treatment inhibited HCC cell proliferation, invasion and tube formation but induced cell apoptosis, accompanied by the decrease of CyclinD1 and N-cadherin expression; however, these effects were counteracted when circ_0013290 expression was increased. MiR-139-5p depletion or MMP16 introduction relieved Matrine-induced effects in HCC cells. The regulation of circ_0013290 toward HCC cell processes involved MMP16. With respect to the mechanism, circ_0013290 acted as a miR-139-5p sponge, and miR-139-5p targeted MMP16 in HCC cells. Besides, circ_0013290 regulated MMP16 expression through miR-139-5p. Further, circ_0013290 depletion enhanced the inhibitory effects of Matrine on tumor tumorigenesis. Conclusion. Matrine inhibited HCC cell malignancy through the circ_0013290/miR-139-5p/MMP16 pathway, suggesting that Matrine is a potential therapeutic agent for HC

Images Speak in Images: A Generalist Painter for In-Context Visual Learning

In-context learning, as a new paradigm in NLP, allows the model to rapidly adapt to various tasks with only a handful of prompts and examples. But in computer vision, the difficulties for in-context learning lie in that tasks vary significantly in the output representations, thus it is unclear how to define the general-purpose task prompts that the vision model can understand and transfer to out-of-domain tasks. In this work, we present Painter, a generalist model which addresses these obstacles with an "image"-centric solution, that is, to redefine the output of core vision tasks as images, and specify task prompts as also images. With this idea, our training process is extremely simple, which performs standard masked image modeling on the stitch of input and output image pairs. This makes the model capable of performing tasks conditioned on visible image patches. Thus, during inference, we can adopt a pair of input and output images from the same task as the input condition, to indicate which task to perform. Without bells and whistles, our generalist Painter can achieve competitive performance compared to well-established task-specific models, on seven representative vision tasks ranging from high-level visual understanding to low-level image processing. In addition, Painter significantly outperforms recent generalist models on several challenging tasks.Comment: Accepted to CVPR 2023. Code and model is available at: https://github.com/baaivision/Painte