A multilevel fuzzy analysis model of higher education teaching quality

Abstract To improve the overall quality of teachers and enhance the teaching ability as well as the teaching quality of institutes of higher learning for the purpose of nurturing high-tech talents, this paper proposes a multilevel fuzzy analysis model of higher education teaching quality based on fuzzy system theory. It constructs a multilevel evaluation system and acquires the fuzzy evaluation set of teaching quality and fuzzy value of a quantity. Through calculation we can get the fuzzy membership between teaching quality and fuzzy evaluation set. Fuzzy membership is applied to standardization according to different types and scales of indicators to get the integrated weighted fuzzy membership. This will realize the evaluation on teaching quality of institutes of higher learning and helps to increase the overall quality of teachers. A case study is introduced to prove the efficacy of the model and the algorithm

Nonalcoholic fatty liver associated with impairment of kidney function in nondiabetes population

Background: Nonalcoholic fatty liver disease (NAFLD) is associated with the increased burden of kidney. However, there is still no large population study to explore the potential relationship between NAFLD and mild kidney function damage (MKFD) after adjusted for confounding factors. This study is to test the hypothesis that NAFLD is associated with MKFD under controlling the effects of confoun-ding factors. Materials and methods: Levels of serum fasting glucose, creatinine, cholesterol, triglyceride, alanine aminotransferase and aspartate aminotransferase were analyzed from 1412 Chinese Han adults. Questionnaire and physical examination were performed to explore the potential association of NAFLD with kidney function. Results: NAFLD was associated with impairment of kidney function. Multivariate-adjusted odds ratio illustrated that, compared to subjects with normal liver, NAFLD subjects had a significantly higher risk of MKFD with or without adjusted for blood glucose and other covariates (P = 0.041). Further results from multi-interaction analysis demonstrated that the underlying mechanisms linked NAFLD with im-paired kidney function may be that they share common risk factors and similar pathological proces-ses. Conclusions: The most striking finding of this study is that NAFLD is negatively associated with kidney function, in nondiabetic population. NAFLD and MKFD may share similar risk factors and/or pathological processes

Enhanced cisplatin chemotherapy sensitivity by self-assembled nanoparticles with Olaparib

Cisplatin (CDDP) is widely used as one kind of chemotherapy drugs in cancer treatment. It functions by interacting with DNA, leading to the DNA damage and subsequent cellular apoptosis. However, the presence of intracellular PARP1 diminishes the anticancer efficacy of CDDP by repairing DNA strands. Olaparib (OLA), a PARP inhibitor, enhances the accumulation of DNA damage by inhibiting its repair. Therefore, the combination of these two drugs enhances the sensitivity of CDDP chemotherapy, leading to improved therapeutic outcomes. Nevertheless, both drugs suffer from poor water solubility and limited tumor targeting capabilities. To address this challenge, we proposed the self-assembly of two drugs, CDDP and OLA, through hydrogen bonding to form stable and uniform nanoparticles. Self-assembled nanoparticles efficiently target tumor cells and selectively release CDDP and OLA within the acidic tumor microenvironment, capitalizing on their respective mechanisms of action for improved anticancer therapy. In vitro studies demonstrated that the CDDP-OLA NPs are significantly more effective than CDDP/OLA mixture and CDDP at penetrating cancer cells and suppressing their growth. In vivo studies revealed that the nanoparticles specifically accumulated at the tumor site and enhanced the therapeutic efficacy without obvious adverse effects. This approach holds great potential for enhancing the drugs’ water solubility, tumor targeting, bioavailability, and synergistic anticancer effects while minimizing its toxic side effects

Emotional Rendering of 3D Indoor Scene with Chinese Elements

One of the challenging tasks is to use computer technology to automatically design a virtual indoor scene that both satisfies realness and matches the target emotion. The subjective nature of emotions brings uncertainty of results. At present, there is a lack of approach to identify and evaluate emotion of indoor scenes. In addition, under the premise of fully considering emotional appeals, the authenticity of scene is also one of important factors in indoor scene design. Aiming at above problems, a novel optimization algorithm combining Chinese elements for indoor scenes rendering is proposed. Firstly, an emotion classifier is trained to identify and evaluate the emotion with the features extracted via deep learning from a indoor scene dataset containing 25000 images. Secondly, in order to ensure the authenticity of rendering results, an algorithm is proposed to evaluate how realistic the colors of the objects’ textures. Next, an algorithm is designed to render indoor scene automatically according to the target emotion. Then, a style transfer algorithm integrating with Chinese elements is used to carry out fine-grained refinement processing on the furnishings in an indoor scene, improve the spatial connotation, cultural connotation and emotional expression of rendering results, and enhance the visual appeal. Finally, the approach is tested in four indoor scenes, and the correctness and effectiveness of the approach are verified through statistical analysis of results and user survey data

Multiobjective Evolutionary Optimization for Prototype-Based Fuzzy Classifiers

Evolving intelligent systems (EISs), particularly, the zero-order ones have demonstrated strong performance on many real-world problems concerning data stream classification, while offering high model transparency and interpretability thanks to their prototype-based nature. Zero-order EISs typically learn prototypes by clustering streaming data online in a “one pass” manner for greater computation efficiency. However, such identified prototypes often lack optimality, resulting in less precise classification boundaries, thereby hindering the potential classification performance of the systems. To address this issue, a commonly adopted strategy is to minimise the training error of the models on historical training data or alternatively, to iteratively minimise the intra-cluster variance of the clusters obtained via online data partitioning. This recognises the fact that the ultimate classification performance of zero-order EISs is driven by the positions of prototypes in the data space. Yet, simply minimising the training error may potentially lead to overfitting, whilst minimising the intra-cluster variance does not necessarily ensure the optimised prototype-based models to attain improved classification outcomes. To achieve better classification performance whilst avoiding overfitting for zero-order EISs, this paper presents a novel multi-objective optimisation approach, enabling EISs to obtain optimal prototypes via involving these two disparate but complementary strategies simultaneously. Five decision-making schemes are introduced for selecting a suitable solution to deploy from the final non-dominated set of the resulting optimised models. Systematic experimental studies are carried out to demonstrate the effectiveness of the proposed optimisation approach in improving the classification performance of zero-order EISs

Multi-Objective Evolutionary Optimisation for Prototype-Based Fuzzy Classifiers

Evolving intelligent systems (EISs), particularly, the zero-order ones have demonstrated strong performance on many real-world problems concerning data stream classification, while offering high model transparency and interpretability thanks to their prototype-based nature. Zero-order EISs typically learn prototypes by clustering streaming data online in a “one pass” manner for greater computation efficiency. However, such identified prototypes often lack optimality, resulting in less precise classification boundaries, thereby hindering the potential classification performance of the systems. To address this issue, a commonly adopted strategy is to minimise the training error of the models on historical training data or alternatively, to iteratively minimise the intra-cluster variance of the clusters obtained via online data partitioning. This recognises the fact that the ultimate classification performance of zero-order EISs is driven by the positions of prototypes in the data space. Yet, simply minimising the training error may potentially lead to overfitting, whilst minimising the intra-cluster variance does not necessarily ensure the optimised prototype-based models to attain improved classification outcomes. To achieve better classification performance whilst avoiding overfitting for zero-order EISs, this paper presents a novel multi-objective optimisation approach, enabling EISs to obtain optimal prototypes via involving these two disparate but complementary strategies simultaneously. Five decision-making schemes are introduced for selecting a suitable solution to deploy from the final non-dominated set of the resulting optimised models. Systematic experimental studies are carried out to demonstrate the effectiveness of the proposed optimisation approach in improving the classification performance of zero-order EISs