Application of Systematic Clustering Method in the Classification and Changes of Atmospheric Ozone in Hunan Province

The cluster analysis method needs continuous improvement and perfection in the research and application of the spatial differentiation and change of pollutants.In this paper,the date of monthly highest concentration of ozone(O_(3))and the concentration value of that day were selected as the similarity coefficient between classes.Single-factor cluster analysis was performed on O_(3)during 2016-2019 and the COVID-19 outbreak of 2020 in Hunan Province using the Ward method.The clustering results showed that the spatial distribution of atmospheric O_(3)in the 14 regions of Hunan Province was most suitable to be classified according to class III clustering areas.That is,the Changsha-Zhuzhou-Xiangtan urban agglomeration was the center,and the high-value area was in northern Hunan.The transition area was in central and southern Hunan,while the low-value area was centered in western Hunan.The partition results were in good agreement with the homogeneous subset of one-way ANOVA and the distribution of monitoring values during the same period.The comparison showed that the inter-class plates in the two periods corresponded well,and the intra-class area showed a continuous geographical distribution,and there were dynamic changes in the spatial differentiation of the O_(3)plates in different periods.In 2020,the center of the O_(3)high-value area plate in Hunan Province moved eastward and extended southward,focusing on the middle and lower reaches of the Xiangjiang River basin,and extending to the upstream area;the regional plate in the transition area expanded significantly;the low-value area plate shrank to the two cities in western Hunan.The abnormal emissions and abnormal climate during the COVID-19 epidemic had an impact on the spatial differentiation of O_(3)in Hunan Province.

Comparative Analysis of Steel Plates and PLA Used for Joint Repair in Humans and Canines

The exploration of fracture internal fixation materials has been one of the research hotspots in the field of biomedical materials.The traditional internal fixation material for fracture is metal fixation.Although its mechanical strength is very large,it can not be degraded and absorbed in human body after implantation of human body or canine joint,which requires a secondary operation to remove,which not only brings pain to patients,but also causes economic pay.[1]Therefore,the development of a biodegradable fracture internal fixation material has become the goal of many researchers.Polylactic acid(PLA)is nontoxic and harmless,has good biocompatibility and strong mechanical properties.It can be degraded in vivo after implantation.The degradation products are CO2 and H2O.[2]For the study of the feasibility of polylactic acid as a substitute for common fracture fixation materials,18 northern Chinese pastoral dogs were randomly divided into blank group,PLA group and plate group.The data were recorded according to the Wakitani score from the first week to the fifteenth week after operation.First,all the indexes were divided into two categories by principal component analysis[3],then the blank group,steel plate group and PLA group were fitted and compared.Finally,it is concluded that PLA is more beneficial to joint repair than steel plate.

Cross-classes domain inference with network sampling for natural resource inventory

There are two distinct types of domains,design-and cross-classes domains,with the former extensively studied under the topic of small-area estimation.In natural resource inventory,however,most classes listed in the condition tables of national inventory programs are characterized as cross-classes domains,such as vegetation type,productivity class,and age class.To date,challenges remain active for inventorying cross-classes domains because these domains are usually of unknown sampling frame and spatial distribution with the result that inference relies on population-level as opposed to domain-level sampling.Multiple challenges are noteworthy:(1)efficient sampling strategies are difficult to develop because of little priori information about the target domain;(2)domain inference relies on a sample designed for the population,so within-domain sample sizes could be too small to support a precise estimation;and(3)increasing sample size for the population does not ensure an increase to the domain,so actual sample size for a target domain remains highly uncertain,particularly for small domains.In this paper,we introduce a design-based generalized systematic adaptive cluster sampling(GSACS)for inventorying cross-classes domains.Design-unbiased Hansen-Hurwitz and Horvitz-Thompson estimators are derived for domain totals and compared within GSACS and with systematic sampling(SYS).Comprehensive Monte Carlo simulations show that(1)GSACS Hansen-Hurwitz and Horvitz-Thompson estimators are unbiased and equally efficient,whereas thelatter outperforms the former for supporting a sample of size one;(2)SYS is a special case of GSACS while the latter outperforms the former in terms of increased efficiency and reduced intensity;(3)GSACS Horvitz-Thompson variance estimator is design-unbiased for a single SYS sample;and(4)rules-ofthumb summarized with respect to sampling design and spatial effect improve precision.Because inventorying a mini domain is analogous to inventorying a rare variable,alternative network sampling procedures are also readily available for inventorying cross-classes domains.

The Main Evaluation Indicators for the Quality of Fruit Radish

The aim of this study was to establish a scientific evaluation system for fruit radish quality. 8 quality indicators were measured for23 varieties,such as Meinong,Qiaotouqing and Chunbulao. The indicators included root weight,root shape index,water content,crispness,soluble solids,crude fiber,isothiocyanate and vitamin C. These data were analyzed by variation correlation analysis,principal component analysis and system cluster analysis. 23 varieties of fruit radish were analyzed by variation correlation; 8 indicators were compressed into 6 integrated variables using principal component analysis. Finally,the main evaluation indicators of fruit radish were determined by using cluster analysis and correlation analysis. The results indicated that the range of variation coefficient was from 1. 64%-89. 99%. Water content,crispness,soluble solids,crude fiber,isothiocyanate were the important components of fruit radish quality,playing a major role in the quality. These 8indicators were closely related and relatively independent. Water content was significantly and positively correlated with crispness; soluble solids and crude fiber were significantly and negatively related to vitamin C; crispness was significantly and positively correlated with crude fiber and vitamin C. Based on the above results,4 representative indicators were used for evaluating fruit radish quality,that is water content,crispness,soluble solids,crude fiber,which could reflect the most information of fruit radish quality.

Establishment of Fungal Decomposition Model Based on OLS and Logistic Model

By using the OLS model,an equation for the rate of decomposing wood by a variety of fungi was established.We analyzed the effects of various fungi in the experimental data under different temperature and humidity.Based on the growth performance of different fungi at different temperatures and humidity,we use the method of systematic cluster to divide the fungi into 5 categories,and introduce competition levels as the viability of different species of fungi.We have established a logistic model that introduces competition levels to obtain a fungal habitat model.The fungal habitat model includes predictions about the relative advantages and disadvantages for each species and combinations of species likely to persist,and do so for different environments including arid,semi-arid,temperate,arboreal,and tropical rain forests.

Trends in disease burden of hepatitis B infection in Jiangsu Province,China,1990-2021

Background:The incidence of hepatitis B virus(HBV)has decreased year by year in China after the expansion of vaccination,but there is still a high disease burden in Jiangsu Province of China.Methods:The year-by-year incidence data of HBV in Jiangsu Province from 1990 to 2021 were collected.The incidence rates of males and females age groups were clustered by systematic clustering,and the incidence rates of each age group were analyzed and studied by using Joinpoint regression model and age-period-cohort effect model(APC).Results:Joinpoint regression model and APC model showed a general decrease in HBV prevalence in both males and females.In addition,the results of the APC model showed that the age,period,and cohort effects of patients all affected the incidence of HBV,and the incidence was higher in males than in females.The incidence is highest in the population between the ages of 15 and 30 years(mean:21.76/100,000),especially in males(mean:31.53/100,000)than in females(mean:11.67/100,000).Another high-risk group is those over 60 years of age(mean:21.40/100,000),especially males(mean:31.17/100,000)than females(mean:11.63/100,000).The period effect of the APC model suggests that HBV vaccination is effective in reducing the incidence of HBV in the population.Conclusions:The incidence of HBV in Jiangsu Province showed a gradual downward trend,but the disease burden in males was higher than that in females.The incidence is higher and increasing rapidly in the population between the ages of 15 and 30 years and people over 60 years of age.More targeted prevention and control measures should be imple-mented for males and the elderly.