Post-pandemic assessment of public knowledge, behavior, and skill on influenza prevention among the general population of Beijing, China

SummaryBackgroundThe aim of this study was to assess the knowledge, behavioral, and skill responses toward influenza in the general population of Beijing after pandemic influenza A (H1N1) 2009.MethodsA cross-sectional study was conducted in Beijing, China, in January 2011. A survey was conducted in which information was collected using a standardized questionnaire. A comprehensive evaluation index system of health literacy related to influenza was built to evaluate the level of health literacy regarding influenza prevention and control among residents in Beijing.ResultsThirteen thousand and fifty-three valid questionnaires were received. The average score for the sum of knowledge, behavior, and skill was 14.12±3.22, and the mean scores for knowledge, behavior, and skill were 4.65±1.20, 7.25±1.94, and 2.21±1.31, respectively. The qualified proportions of these three sections were 23.7%, 11.9%, and 43.4%, respectively, and the total proportion with a qualified level was 6.7%. There were significant differences in health literacy level related to influenza among the different gender, age, educational level, occupational status, and location groups (p<0.05). There was a significant association between knowledge and behavior (r=0.084, p<0.001), and knowledge and skill (r=0.102, p<0.001).ConclusionsThe health literacy level remains low among the general population in Beijing and the extent of relativities in knowledge, behavior, and skill about influenza was found to be weak. Therefore, improvements are needed in terms of certain aspects, particularly for the elderly and the population of rural districts. Educational level, as a significant factor in reducing the spread of influenza, should be considered seriously when intervention strategies are implemented

A deep learning model for drug screening and evaluation in bladder cancer organoids

Three-dimensional cell tissue culture, which produces biological structures termed organoids, has rapidly promoted the progress of biological research, including basic research, drug discovery, and regenerative medicine. However, due to the lack of algorithms and software, analysis of organoid growth is labor intensive and time-consuming. Currently it requires individual measurements using software such as ImageJ, leading to low screening efficiency when used for a high throughput screen. To solve this problem, we developed a bladder cancer organoid culture system, generated microscopic images, and developed a novel automatic image segmentation model, AU2Net (Attention and Cross U2Net). Using a dataset of two hundred images from growing organoids (day1 to day 7) and organoids with or without drug treatment, our model applies deep learning technology for image segmentation. To further improve the accuracy of model prediction, a variety of methods are integrated to improve the model’s specificity, including adding Grouping Cross Merge (GCM) modules at the model’s jump joints to strengthen the model’s feature information. After feature information acquisition, a residual attentional gate (RAG) is added to suppress unnecessary feature propagation and improve the precision of organoids segmentation by establishing rich context-dependent models for local features. Experimental results show that each optimization scheme can significantly improve model performance. The sensitivity, specificity, and F1-Score of the ACU2Net model reached 94.81%, 88.50%, and 91.54% respectively, which exceed those of U-Net, Attention U-Net, and other available network models. Together, this novel ACU2Net model can provide more accurate segmentation results from organoid images and can improve the efficiency of drug screening evaluation using organoids

Hesitant Fuzzy Linguistic Agglomerative Hierarchical Clustering Algorithm and Its Application in Judicial Practice

The fuzzy clustering algorithm has become a research hotspot in many fields because of its better clustering effect and data expression ability. However, little research focuses on the clustering of hesitant fuzzy linguistic term sets (HFLTSs). To fill in the research gaps, we extend the data type of clustering to hesitant fuzzy linguistic information. A kind of hesitant fuzzy linguistic agglomerative hierarchical clustering algorithm is proposed. Furthermore, we propose a hesitant fuzzy linguistic Boole matrix clustering algorithm and compare the two clustering algorithms. The proposed clustering algorithms are applied in the field of judicial execution, which provides decision support for the executive judge to determine the focus of the investigation and the control. A clustering example verifies the clustering algorithm’s effectiveness in the context of hesitant fuzzy linguistic decision information

An Emergency Quantity Discount Contract with Supplier Risk Aversion under the Asymmetric Information of Sales Costs

In the circumstance that unexpected events lead to the information asymmetry of sales costs, supplier risk aversion and stochastic price, this paper discusses the internal law of using an emergency quantity discount contract to coordinate the supply chain. First, the Conditional Value at Risk (CVaR) model of supplier risk aversion under the condition of information symmetry is constructed. In addition, the model is extended to the game model of the CVaR of supplier risk aversion under the condition of the asymmetric information of sales costs and solved. After that, the simulation test is performed. The results show that, firstly, under the condition of random price, the supplier risk aversion leads to the phenomenon of bifurcation and mutation in each decision variable of the supply chain system. Secondly, retailers can obtain excess profits by concealing private information, but this harms the interests of suppliers and the entire supply chain. Thirdly, suppliers with different risk attitudes should have different strategies concerning asymmetry in sales cost information. Fourthly, the more asymmetric the information for the sales costs, the more unstable the system. The conclusion is that the phenomenon of bifurcation mutation is the result of the coupling effect of price randomness and supplier risk aversion. The supply chain cannot coordinate in the bifurcation mutation region, but can coordinate outside of it. Hiding private information benefits those who own it, but harms the system as a whole

Hybrid TODIM Method for Law Enforcement Possibility Evaluation of Judgment Debtor

The phenomenon of the judgment debtor evading the execution of legal documents and concealing his property by improper means has become increasingly prominent in China, which seriously affects the realization of the people&rsquo;s legitimate rights and interests. To protect the legitimate rights and interests of the people, it is necessary to study the law enforcement possibility evaluation of judgment debtors and quickly judge which judgment debtor is likely to complete the legal documents. A novel hybrid TODIM (an acronym in Portuguese for Interative Multi-criteria Decision Making) method for evaluating the law enforcement possibility of judgment debtors is developed. The main idea of the hybrid TODIM method is to obtain the relative possibility value of judgment debtors by comparing the attribute values between two judgment debtors and aggregating all the attributes&rsquo; differences. The result shows that the hybrid TODIM method fully considers the psychological and behavioral factors of the law enforcement officers in the evaluation process. The evaluation result is more in line with the law enforcement officers&rsquo; experience in handling execution cases. Compared with the hybrid TOPSIS (technique for order preference by similarity to ideal solution) method, the hybrid TODIM method is more suitable for solving the problem