Abstract: Objective To analyze the distribution characteristics of new occupational disease cases in Guangxi Zhuang “ ” Methods Autonomous Region (hereinafter referred to as Guangxi ) from 2016 to 2021. Through the Occupational Disease Report Card of Occupational Disease and Occupational Health Monitoring Information System, a subsystem of China Disease Prevention and Control Information System, the data of occupational disease reported in Guangxi from 2016 to 2021 were Results collected and analyzed by routine data analytic method. A total of 633 new cases of occupational diseases were diagnosed in Guangxi from 2016 to 2021. Most of the cases occurred in males that account for 96.5% (611/633). Among them, 85.8% of cases were occupational pneumoconiosis, 6.3% occupational otoaryngological and stomaological diseases, 3.0% chemical poisoning and 4.9% other five types of occupational diseases. The geographical distribution was dominated in Hechi Citythataccountsfor51.7%.Theindustrialdistributionwasconcentratedinbituminouscoalminingandwashing,tinminingand dressing, lead and zinc mining and dressing (43.1% of the total). Private enterprises account for 47.3%. The enterprise was - mainlysmall sizedenterprises,accountingfor50.0%.Themaintypesofworkwererockdrillsandmaincoalminer,accountingConclusion for18.8%and17.5%,respectively. Occupationalpneumoconiosiswasthemostimportantoccupationaldiseasein Guangxi. It is necessary to strengthen the occupational hazard exposure control and protection of bituminous coal mining and - - washing,tinmininganddressing,leadandzincmininganddressingindustries,privateenterprises,andsmall andmedium sized enterprises,rockdrillsandmaincoalminerinHechiCity.

Early detection and timely intervention are very essential for autism. This paper used the wavelet transform and empirical mode decomposition (EMD) to extract the features of electroencephalogram (EEG), to compare the feature differences of EEG between the autistic children and healthy children. The experimental subjects included 25 healthy children (aged 5-10 years old) and 25 children with autism (20 boys and 5 girls aged 5-10 years old) respectively. The alpha, beta, theta and delta rhythm wave spectra of the C3, C4, F3, F4, F7, F8, FP1, FP2, O1, O2, P3, P4, T3, T4, T5 and T6 channels were extracted and decomposed by EMD decomposition to obtain the intrinsic modal functions. Finally the support vector machine (SVM) classifier was used to implement assessment of autism and normal classification. The results showed that the accuracy could reach 87% and which was nearly 20% higher than that of the model combining the wavelet transform and sample entropy in the paper. Moreover, the accuracy of delta (1-4 Hz) rhythm wave was the highest among the four kinds of rhythms. And the classification accuracy of the forehead F7 channel, left FP1 channel and T6 channel in the temporal region were all up to 90%, which expressed the characteristics of EEG signals in autistic children better.