Evaluation of Wind Turbine Operation Status Based on ACO + FAHP

Aiming at the shortcomings of the fuzzy analytic hierarchy process (FAHP) in the comprehensive evaluation of wind power projects, such as the diffi culty of satisfying and modifying the consistency of the judgment matrix and the high computational complexity, a fuzzy analytic hierarchy process based on ant colony optimization (ACO+FAHP) is proposed. Firstly, the proposed fuzzy analytic hierarchy process based on ant colony optimization algorithm overcomes the disadvantages that the weight and consistency cannot be improved once the judgment matrix is given. The comparison chart of the consistency ratio calculated according to this method shows that the consistency ratio B, C1-C5 all have diff erent degrees of reduction. Then, in view of the fact that various qualitative indicators cannot be accurately calculated, the wind turbine operating status evaluation model is established by using the fuzzy comprehensive evaluation method. In this paper, the evaluation score of a certain wind farm is 0.731, which means that the operators need to carry out high-level maintenance at this time

Vaccination in the childhood and awareness of basic public health services program among internal migrants: a nationwide cross-sectional study

BACKGROUND: Vaccination is proved to be one of the most effective and efficient way to prevent illness and reduce health inequality. Studies about association between vaccination inequalities in the childhood and awareness of basic public health services program among internal migrants in China are lacking. In this study, we aimed to explore the association between migrants' vaccination status between 0 and 6 years old and their awareness of the National Basic Public Health Services (BPHSs) project in China. METHODS: We included 10,013 respondents aged 15 years old or above of eight provinces from 2017 Migrant Population Dynamic Monitoring Survey in China, a nationwide cross-sectional study. Univariate and multivariable logistic regressions were used to assess vaccination inequalities and the awareness of public health information. RESULTS: Only 64.8% migrants were vaccinated in their childhood, which is far below the goal of national requirement of 100% vaccination. This also indicated the vaccination inequalities among migrants. Female, the middle-aged, married or having a relationship, the highly educated and the healthy population had higher awareness of this project than others. Both univariate and multivariate logistic regressions showed greatly significant association between vaccination status and some vaccines. Specifically, after adding convariates, the results showed that there were significant associations between the vaccination rates of eight recommended vaccines in the childhood and their awareness of BPHSs project (all p values < 0.001), including HepB vaccine (OR: 1.28; 95%CI: 1.19, 1.37), HepA vaccine (OR: 1.27; 95%CI: 1.15, 1.41), FIn vaccine (OR: 1.28; 95%CI: 1.16, 1.45), JE vaccine (OR: 1.14; 95%CI: 1.04, 1.27), TIG vaccine (OR: 1.27; 95%CI: 1.05, 1.47), DTaP vaccine (OR: 1.30; 95%CI: 1.11-1.53), MPSV vaccine (OR: 1.26; 95%CI: 1.07-1.49), HF vaccine (OR: 1.32; 95%CI: 1.11, 1.53), except for RaB vaccine (OR: 1.07; 95%CI: 0.89, 1.53). CONCLUSIONS: The vaccination inequalities exist among migrants. There is a strong relationship between the vaccination status in the childhood and the awareness rate of BPHSs project among migrants. From our findings we could know that the promotion of vaccination rates of the disadvantaged population such as the internal migrants or other minority population can help them increase the awareness of free public health services, which was proved to be beneficial for health equity and effectiveness and could promote public health in the future

Association between Social Integration, Social Exclusion, and Vaccination Behavior among Internal Migrants in China: A Cross-Sectional Study

Cross-sectional studies about the association between social integration, social exclusion, and vaccination behavior among internal migrants in China are lacking. In this study, we aimed to explore the association between the influenza vaccination behavior and social integration as well as social exclusion in China based on a cross-sectional study. We included 12,467 participants aged 15 years old or above from the 2017 Migrant Population Dynamic Monitoring Survey (MDMS). We used univariate analysis and logistic regression models to access the association between social integration, exclusion status, and influenza vaccination rates. Results suggested that the association between social integration and the vaccination rate was significantly positive. Moving between different districts impact on people’s mental health and their health performance. Significant association between influenza vaccination behavior and education attainment, income status, health record, and awareness of basic public health services program was reported. Therefore, in order to reduce the incidence of influenza disease and increase the vaccination rate, policymakers and the public should promote social integration for internal migrants. Meanwhile, our finding also implies possible strategies to promote COVID-19 vaccination

SingVisio: Visual Analytics of Diffusion Model for Singing Voice Conversion

In this study, we present SingVisio, an interactive visual analysis system that aims to explain the diffusion model used in singing voice conversion. SingVisio provides a visual display of the generation process in diffusion models, showcasing the step-by-step denoising of the noisy spectrum and its transformation into a clean spectrum that captures the desired singer's timbre. The system also facilitates side-by-side comparisons of different conditions, such as source content, melody, and target timbre, highlighting the impact of these conditions on the diffusion generation process and resulting conversions. Through comprehensive evaluations, SingVisio demonstrates its effectiveness in terms of system design, functionality, explainability, and user-friendliness. It offers users of various backgrounds valuable learning experiences and insights into the diffusion model for singing voice conversion

Prediction of IDH1 gene mutation by a nomogram based on multiparametric and multiregional MR images

Objective: To investigate the value of a nomogram based on multiparametric and multiregional MR images to predict Isocitrate Dehydrogenase-1 (IDH1) gene mutations in glioma. Data and methods: The authors performed a retrospective analysis of 110 MR images of surgically confirmed pathological gliomas; 33 patients with IDH1 gene Mutation (IDH1-M) and 77 patients with Wild-type IDH1 (IDH1-W) were divided into training and validation sets in a 7:3 ratio. The clinical features were statistically analyzed using SPSS and R software. Three glioma regions (rCET, rE, rNEC) were outlined using ITK-SNAP software and projected to four conventional sequences (T1, T2, Flair, T1C) for feature extraction using AI-Kit software. The extracted features were screened using R software. A logistic regression model was established, and a nomogram was generated using the selected clinical features. Eight models were developed based on different sequences and ROIs, and Receiver Operating Characteristic (ROC) curves were used to evaluate the predictive efficacy. Decision curve analysis was performed to assess the clinical usefulness. Results: Age was selected with Radscore to construct the nomogram. The Model 1 AUC values based on four sequences and three ROIs were the highest in these models, at 0.93 and 0.89, respectively. Decision curve analysis indicated that the net benefit of model 1 was higher than that of the other models for most Pt-values. Conclusion: A nomogram based on multiparametric and multiregional MR images can predict the mutation status of the IDH1 gene accurately

Strontium chloride improves bone mass by affecting the gut microbiota in young male rats

IntroductionBone mass accumulated in early adulthood is an important determinant of bone mass throughout the lifespan, and inadequate bone deposition may lead to associated skeletal diseases. Recent studies suggest that gut bacteria may be potential factors in boosting bone mass. Strontium (Sr) as a key bioactive element has been shown to improve bone quality, but the precise way that maintains the equilibrium of the gut microbiome and bone health is still not well understood.MethodsWe explored the capacity of SrCl2 solutions of varying concentrations (0, 100, 200 and 400 mg/kg BW) on bone quality in 7-week-old male Wistar rats and attempted to elucidate the mechanism through gut microbes.ResultsThe results showed that in a Wistar rat model under normal growth conditions, serum Ca levels increased after Sr-treatment and showed a dose-dependent increase with Sr concentration. Three-point mechanics and Micro-CT results showed that Sr exposure enhanced bone biomechanical properties and improved bone microarchitecture. In addition, the osteoblast gene markers BMP, BGP, RUNX2, OPG and ALP mRNA levels were significantly increased to varying degrees after Sr treatment, and the osteoclast markers RANKL and TRAP were accompanied by varying degrees of reduction. These experimental results show that Sr improves bones from multiple angles. Further investigation of the microbial population revealed that the composition of the gut microbiome was changed due to Sr, with the abundance of 6 of the bacteria showing a different dose dependence with Sr concentration than the control group. To investigate whether alterations in bacterial flora were responsible for the effects of Sr on bone remodeling, a further pearson correlation analysis was done, 4 types of bacteria (Ruminococcaceae_UCG-014, Lachnospiraceae_NK4A136_group, Alistipes and Weissella) were deduced to be the primary contributors to Sr-relieved bone loss. Of these, we focused our analysis on the most firmly associated Ruminococcaceae_UCG-014.DiscussionTo summarize, our current research explores changes in bone mass following Sr intervention in young individuals, and the connection between Sr-altered intestinal flora and potentially beneficial bacteria in the attenuation of bone loss. These discoveries underscore the importance of the “gut-bone” axis, contributing to an understanding of how Sr affects bone quality, and providing a fresh idea for bone mass accumulation in young individuals and thereby preventing disease due to acquired bone mass deficiency

MD-Dose: A Diffusion Model based on the Mamba for Radiotherapy Dose Prediction

Radiation therapy is crucial in cancer treatment. Experienced experts typically iteratively generate high-quality dose distribution maps, forming the basis for excellent radiation therapy plans. Therefore, automated prediction of dose distribution maps is significant in expediting the treatment process and providing a better starting point for developing radiation therapy plans. With the remarkable results of diffusion models in predicting high-frequency regions of dose distribution maps, dose prediction methods based on diffusion models have been extensively studied. However, existing methods mainly utilize CNNs or Transformers as denoising networks. CNNs lack the capture of global receptive fields, resulting in suboptimal prediction performance. Transformers excel in global modeling but face quadratic complexity with image size, resulting in significant computational overhead. To tackle these challenges, we introduce a novel diffusion model, MD-Dose, based on the Mamba architecture for predicting radiation therapy dose distribution in thoracic cancer patients. In the forward process, MD-Dose adds Gaussian noise to dose distribution maps to obtain pure noise images. In the backward process, MD-Dose utilizes a noise predictor based on the Mamba to predict the noise, ultimately outputting the dose distribution maps. Furthermore, We develop a Mamba encoder to extract structural information and integrate it into the noise predictor for localizing dose regions in the planning target volume (PTV) and organs at risk (OARs). Through extensive experiments on a dataset of 300 thoracic tumor patients, we showcase the superiority of MD-Dose in various metrics and time consumption

Zoom Out and Observe: News Environment Perception for Fake News Detection

Fake news detection is crucial for preventing the dissemination of misinformation on social media. To differentiate fake news from real ones, existing methods observe the language patterns of the news post and "zoom in" to verify its content with knowledge sources or check its readers' replies. However, these methods neglect the information in the external news environment where a fake news post is created and disseminated. The news environment represents recent mainstream media opinion and public attention, which is an important inspiration of fake news fabrication because fake news is often designed to ride the wave of popular events and catch public attention with unexpected novel content for greater exposure and spread. To capture the environmental signals of news posts, we "zoom out" to observe the news environment and propose the News Environment Perception Framework (NEP). For each post, we construct its macro and micro news environment from recent mainstream news. Then we design a popularity-oriented and a novelty-oriented module to perceive useful signals and further assist final prediction. Experiments on our newly built datasets show that the NEP can efficiently improve the performance of basic fake news detectors.Comment: ACL 2022 Main Conference (Long Paper