ABSTRACT
Objective@#To analyze the delay on detection, care seeking, diagnosis and treatment of tuberculosis among students in Inner Mongolia Autonomous Region (Inner Mongolia) from 2011 to 2022 and its influencing factors, so as to provide support for the prevention and treatment of tuberculosis among students.@*Methods@#The general demographic indicators of students with tuberculosis in Inner Mongolia from January 1, 2011 to December 31, 2022 were collected from the infectious disease monitoring (new) module of the China Disease Prevention and Control Information System. General characteristics and trend of four types of delayed pulmonary tuberculosis patients in students were analyzed. The influencing factors were analyzed using univariate and multivariate Logistic regressions.@*Results@#From 2011 to 2022, there were 6 032 cases of pulmonary tuberculosis among students in Inner Mongolia. The rates of delayed detection, delayed care seeking, delayed diagnosis, and delayed treatment were 51.71%, 64.01%, 7.82 %, and 2.30%, respectively. The results of multivariate Logistic regression analysis showed that tracking ( OR =1.51) in the patient source,league level diagnosis ( OR =3.16) in the diagnostic institution level,and county level diagnosis ( OR =2.41) were positively associated with delayed discovery ( P <0.05). At the level of diagnostic unit, league city level diagnosis ( OR =2.69), and county level diagnosis ( OR =3.67) associated with more delayed care seeking ( P <0.05). Referral ( OR =1.58) and follow up ( OR =2.55), floating population ( OR =2.05), further consultation with a doctor ( OR =2.11), and no results in imaging manifestations ( OR =2.19) were positively associated with delayed diagnosis( P <0.05). The factors contributing to delayed treatment were referral ( OR =1.84), follow up ( OR =4.91), active screening ( OR =5.46), and floating population( OR =1.95)( P <0.05).@*Conclusions@#From 2011 to 2022, the delay on detection and care seeking for tuberculosis patients among students in Inner Mongolia is at a relatively high level, while the delay in diagnosis and treatment is at a relatively low level but shows an increasing trend. It is necessary to focus on the factors associated with delays in identification, diagnosis and treatment in tuberculosis outbreak in the context of school to prevent or reduce school tuberculosis outbreak.
ABSTRACT
ABSTRACT: The use of machine vision to recognize mature pomegranates in natural environments is of major significance in improving the applicability and work efficiency of picking robots. By analyzing the color characteristics of color images of mature pomegranates under different illumination conditions, the feasibility of the YCbCr color model for pomegranate image recognition under different illumination conditions was proven. First, the Cr component map of pomegranate image is selected and then the pomegranate fruit is segmented by the kernel fuzzy C-means clustering algorithm to obtain the pomegranate image. Contrast experiments of pomegranate image segmentation under different illumination conditions were then performed using the proposed kernel fuzzy C-means clustering algorithm, the fuzzy C-means clustering algorithm, the Otsu algorithm and the threshold segmentation algorithm. Results of the experiments verified the effectiveness and superiority of the proposed algorithm.
RESUMO: O uso de máquina para reconhecer romãs maduras em ambientes naturais é de grande importância para melhorar a aplicabilidade e a eficiência do trabalho de robôs de colheita. Ao analisar as características de cor das imagens coloridas de romãs maduras sob diferentes condições de iluminação, a viabilidade do modelo de cores YCbCr para o reconhecimento de imagens de romãs sob diferentes condições de iluminação foi comprovada. Primeiro, o mapa do componente Cr da imagem da romã é selecionado e, em seguida, o fruto da romãzeira é segmentado pelo algoritmo de agrupamento C-means fuzzy do kernel para obter a imagem da romã. Experimentos contrastados de segmentação de imagens de romã sob diferentes condições de iluminação foram então realizados usando o algoritmo proposto de agrupamento C-means fuzzy, o algoritmo fuzzy de agrupamento C-means, o algoritmo Otsu e o algoritmo de segmentação de limiares. Os resultados dos experimentos verificaram a efetividade e superioridade do algoritmo proposto.