A study of methods to stimulate students’ problem awareness during university physics lectures

In the context of the active promotion of quality education, the analysis of the current process of cultivating the comprehensive ability of science and technology college students from the teachers’ perspective often reveals many problems. Many students have half-understood knowledge, which is ultimately caused by the lack of problem awareness and independent thinking. If we want to develop students’ physical thinking and improve their overall quality, we need to adjust our teaching methods. This paper carefully analyzes the possible problems of teachers and students in university physics teaching and their causes, and gives scientifi c and reasonable improvement measures, in order to promote the comprehensive development of college students

Pattern Based Information Extraction System in Business News Articles

Business news journals provide a rich resource of business events, which enable domain experts to further understand the spatio-temporal changes occur among a set of firms and people. However, extracting structured data from journal resource that is text-based and unstructured is a non-trivial challenge. This project designs and implements a Business Information Extraction System, which combines advanced natural language processing (NLP) tools and knowledge-based extraction patterns to process and extract information of target business event from news journals automatically. The performance evaluation on the proposed system suggests that IE techniques works well on business event extraction and it is promising to apply the technique to extract more types of business events.Master of Science in Information Scienc

Antifungal Drug Formulation for Vaginal Yeast Infection

Over 138 million women worldwide are affected by recurrent yeast vaginitis or Candidiasis [1]. Candidiasis is a type of infection caused by yeast Candida albicans overgrowth, and it is one of the most common diseases affecting women. Three out of four women will have a yeast infection during their lifetime, and most women experience infection at least two times [2]. Yeast infection is often mild with symptoms including vaginal itching, pain or discomfort during urinating, redness, and swelling in the vagina. To alleviate this infection, oral doses of fluconazole and vaginal doses of miconazole in the form of a suppository are prescribed. Sulconazole is currently used as a topical antifungal treatment for skin infections. Miconazole is reported with adverse effects such as diarrhea, headache, nausea [31]. Even though there are no adverse effects of low-dose fluconazole, patients experience adverse effects when prescribing with high-dose fluconazole daily [32]. Adverse effects of sulconazole include redness, irritation, and contact dermatitis [33]. Considering that oral administrated drugs need to circulate systemically, vaginal delivery is more optimal due to dosing directly to the site of infection. On the other hand, vaginal administration would have higher efficiency in drug delivery due to its local effect on the vaginal microbiota. To formulate drugs for vaginal delivery, nanosuspension of drugs under 300 nm can help to get through mucus layer for better absorption. Antifungal drugs fluconazole, miconazole, and sulconazole were formulated in nanosuspension using various Pluronic block copolymers as stabilizers. All antifungal drugs were tested in vitro to assess potency. The effectiveness of antifungal drugs in treating vaginal C. albicans infection in mice was then tested in vivo. Antifungal drugs formulated fluconazole and sulconazole showed effectiveness in killing C. albicans both in vitro and in vivo, however, formulated miconazole only showed effectiveness in killing C. albicans in vitro. More studies are needed

Physics-guided Noise Neural Proxy for Low-light Raw Image Denoising

Low-light raw image denoising plays a crucial role in mobile photography, and learning-based methods have become the mainstream approach. Training the learning-based methods with synthetic data emerges as an efficient and practical alternative to paired real data. However, the quality of synthetic data is inherently limited by the low accuracy of the noise model, which decreases the performance of low-light raw image denoising. In this paper, we develop a novel framework for accurate noise modeling that learns a physics-guided noise neural proxy (PNNP) from dark frames. PNNP integrates three efficient techniques: physics-guided noise decoupling (PND), physics-guided proxy model (PPM), and differentiable distribution-oriented loss (DDL). The PND decouples the dark frame into different components and handles different levels of noise in a flexible manner, which reduces the complexity of the noise neural proxy. The PPM incorporates physical priors to effectively constrain the generated noise, which promotes the accuracy of the noise neural proxy. The DDL provides explicit and reliable supervision for noise modeling, which promotes the precision of the noise neural proxy. Extensive experiments on public low-light raw image denoising datasets and real low-light imaging scenarios demonstrate the superior performance of our PNNP framework