Study of Mechanics and Durability of Non-Spontaneous Combustion Coal Gangue Coarse-Aggregate High-Performance Concrete.

The coal gangue coarse-aggregate content in ordinary concrete should not be too large. In order to further improve the utilization rate of coal gangue coarse aggregate, this study used the principle of "strong wrapped weak" to prepare high-performance concrete. This study considered four factors, namely, water-binder (W/B) ratios, non-spontaneous combustion coal gangue (NCCG) coarse-aggregate contents, fly ash-slag mass ratios, and silica fume coating to prepare high-performance concrete. The workability, mechanical, and durability properties were studied, and the changes in the interfacial transition zone (ITZ) of concrete before and after sulfate attack and freeze-thaw cycles were analyzed based on the SEM test. The life prediction of NCCG coarse-aggregate high-performance concrete was carried out based on the grey system GM(1,1) prediction model. The results show that the NCCG coarse-aggregate contents have the greatest effect on compressive strength, sulfate resistance, and frost resistance. The W/B ratio has the greatest effect on the anti-carbonization properties. Fly ash-slag mixing can obtain better durability. Considering the effect on the design service life of high-performance concrete, NCCG coarse aggregate is used to prepare high-performance concrete in North China, and the recommended content is 60%; in the Northwest and Northeast regions, the recommended content is 45%. This study provides a basis for the preparation of high-performance concrete with NCCG coarse aggregate.

Trends and predictions in the physical shape of Chinese preschool children from 2000 to 2020.

Objective: To explore physical shape changes in preschool children from 2000 to 2020, and forecast development trends over the next 10 years. Method: The grey GM (1,1) prediction model was used to fit the physical shape indicators of preschool children in China from 2000 to 2020, and then the longitudinal change trend of physical shape was compared and analyzed. Finally, the development trend of physical shape in China in 2025 and 2030 was predicted. Results: (1) During the period from 2000 to 2020, the height, weight and chest circumference of Chinese preschool children all increased rapidly. Specifically, the weight of male and female children increased by 1.8 kg and 1.6 kg, their chest circumference increased by 1.6 cm and 1.5 cm, respectively, and both their heights increased by 3.6 cm. Among these indicators, the older the age, the greater the growth rate. It is expected that all the indicators will continue to grow rapidly over the next 10 years, but the growth rate will slow. (2) From 2000 to 2020, the growth rate of weight was higher than that of height, and BMI showed an increasing trend. The obesity detection rates in boys and girls increased by 5.6 and 2.8%, respectively. Over the next 10 years, the incidence of obesity is expected to increase by 3.8% in boys and 2.8% in girls. (3) Improvement in the growth and development of preschool children in China has a certain correlation with the rapid growth of China's economy,less physical activity, education and other factors. Conclusion: Over the past 20 years, the growth and nutritional status of Chinese preschoolers have improved dramatically, but overweight and obesity remain. Overweight and obesity rates are expected to continue to increase rapidly over the next 10 years, particularly among boys, and effective measures should be taken to control the obesity epidemic.

Spatio-temporal evolution and trend prediction of urban ecosystem service value based on CLUE-S and GM (1,1) compound model.

Ecosystem service value (ESV) is a significant indicator related to regional ecological well-being. Evaluating ESV premised on continuous time series land benefit data can provide an accurate reference for regional ecological civilization construction and sustainable development. Taking Shijiazhuang, the capital city of Hebei Province as an example, the study analyzed land use changes based on the land use data of the continuous time series from 2000 to 2020 and introduced a socio-economic adjustment factor and biomass factor adjustment factor to construct a dynamic assessment model of ecosystem service value. The spatiotemporal changes of the ecosystem service value in Shijiazhuang City were evaluated, and the dynamic prediction of the ecosystem service value was made using the CLUE-S model and the GM (1,1) model. (1) The changes in the overall ESV and spatial pattern in Shijiazhuang are strongly linked to the change in land use, and the contribution of cultivated land, woodland, and grassland to ecosystem service value exceeds 90%. (2) Between 2000 and 2020, the value of ecosystem services illustrated a dynamic change and gradually declined, with the total amount falling from 28.003 to 19.513 billion yuan. Among individual ecosystem services, the value of regulation services suffered the most serious loss. (3) CLUE-S and GM (1,1) perform well in the prediction of ESV. The prediction outcomes illustrate that the ecosystem service value of Shijiazhuang will continue to decline by 2025, and the ecosystem value will drop to 16.771 billion yuan. This research may offer a reference for the dynamic assessment of ESV of the continuous sequence and help to promote regional ecological protection and sustainable development.

The future trajectory of carbon emissions in the process of carbon neutrality in South Korea.

The excessive use of fossil energy in industrialization has caused the frequent occurrence of global warming and environmental pollution issues, which seriously threaten the sustainable social and economic development of South Korea and other countries. In response to the international community's call to effectively address climate change, South Korea has announced achieving carbon neutrality by 2050. In this context, this paper takes the carbon emission of South Korea from 2016 to 2021 as a sample and focuses on using the GM(1,1) model to predict the carbon emission change trajectory of South Korea in the process of achieving carbon neutrality. The results show: first, in the process of carbon neutrality, South Korea's carbon emissions show a downward trend, with an average annual rate of 2.34%. Second, by 2030, carbon emissions will decline to 502.34 Mt CO2e, down about 26.79% from the 2018 peak. By 2050, South Korea's carbon emissions will decline to 312.65 Mt CO2e, down about 54.44% from the 2018 peak. Third, it is difficult for South Korea to achieve its carbon neutrality target by 2050 based solely on its forest carbon sink storage capacity. Therefore, this study is expected to provide a reference for improving the carbon neutrality promotion strategy in South Korea and strengthening the construction of relevant systems of carbon neutrality, and so can provide some reference for other countries, including China, to improve policy design to promote the green and low-carbon transformation of the global economy.

Data-driven adaptive GM(1,1) time series prediction model for thermal comfort.

In this paper, the future prediction of predicted mean vote (PMV) index of indoor environment is studied. PMV is the evaluation index used in this paper to represent the thermal comfort of human body. According to the literature, the main environmental factors affecting PMV index are temperature, humidity, black globe temperature, wind speed, average radiation temperature, and clothing surface temperature, and there is a complex nonlinear relationship between the six variables. Due to the coupling relationship between the six parameters, the PMV formula can be simplified under specific conditions, reducing the monitoring of variables that are difficult to observe. Then, the improved grey system prediction model GM(1,1) with optimized selection dimension is used to predict the future time of PMV. Due to the irregularity, uncertainty and fluctuation of PMV values in time series, based on the original GM(1,1) time series prediction, an adaptive GM(1,1) improved model is proposed, which can continuously change with time series and enhance its prediction accuracy. By contrast, the improved GM(1,1) model can be derived from the sliding window of the adaptive model through changes in the dataset and get better model grades. It lays a foundation for the future research on the predicted index of PMV, so as to set and control the air conditioning system in advance, to meet the intelligence of modern intelligent home and humanized function of sensing human comfort.

Time series prediction for the epidemic trends of monkeypox using the ARIMA, exponential smoothing, GM (1, 1) and LSTM deep learning methods.

Monkeypox is a critical public health emergency with international implications. Few confirmed monkeypox cases had previously been reported outside endemic countries. However, since May 2022, the number of monkeypox infections has increased exponentially in non-endemic countries, especially in North America and Europe. The objective of this study was to develop optimal models for predicting daily cumulative confirmed monkeypox cases to help improve public health strategies. Autoregressive integrated moving average (ARIMA), exponential smoothing, long short-term memory (LSTM) and GM (1, 1) models were employed to fit the cumulative cases in the world, the USA, Spain, Germany, the UK and France. Performance was evaluated by minimum mean absolute percentage error (MAPE), among other metrics. The ARIMA (2, 2, 1) model performed best on the global monkeypox dataset, with a MAPE value of 0.040, while ARIMA (2, 2, 3) performed the best on the USA and French datasets, with MAPE values of 0.164 and 0.043, respectively. The exponential smoothing model showed superior performance on the Spanish, German and UK datasets, with MAPE values of 0.043, 0.015 and 0.021, respectively. In conclusion, an appropriate model should be selected according to the local epidemic characteristics, which is crucial for monitoring the monkeypox epidemic. Monkeypox epidemics remain severe, especially in North America and Europe, e.g. in the USA and Spain. The development of a comprehensive, evidence-based scientific programme at all levels is critical to controlling the spread of monkeypox infection.

Machine learning assessment of white blood cell counts in workers exposed to benzene: a historical cohort study.

To explore the fitting effect of the ARIMA, GM(1,1), and RANSAC model in the changes of white blood cells (WBC) in benzene-exposed workers, and select the optimal model to predict the WBC count of workers. Among 350 employees in an aerospace process manufacturing enterprise in Nanjing, workers with 10 years of benzene exposure were selected, and used Excel software to organize the WBC data, and the ARIMA model and RANSAC model were established by R software, and the GM(1, 1) model was established by DPS software, and the magnitude of the mean absolute percentage error (MAPE) of fitting three models to WBC counts was compared. The MAPE based on the ARIMA(2,1,2) model is 6.78%, the MAPE based on the GM(1,1) model is 5.19%, and the MAPE based on the RANSAC model is 6.37%, so the GM( 1,1) model was more suitable for fitting the trend of WBC counts in benzene exposed workers in this study. The GM(1,1) model is suitable for fitting WBC counts in a small sample size and can provide a short-term prediction of WBC counts in benzene-exposed workers and provide basic information for occupational health risk assessment of workers.

Analysis of carbon emission drivers of secondary industries in Energy "Golden Triangle" area based on LMDI and two-dimensional decoupling model.

As an essential energy and chemical base in China, carbon reduction in the Energy "Golden Triangle" (EGT) area is significant. This paper used the logarithmic mean Divisia index (LMDI) method to analyze the drivers of carbon emissions from secondary industry energy consumption (CESEC) in EGT from 2005 to 2019 and then used the GM (1,1) method to simulate carbon emissions in 2030. Meanwhile, the decoupling relationship between carbon emissions and economic development was also analyzed using the two-dimensional decoupling model to test the effectiveness of carbon reduction by the region's government. This paper showed the following: (1) CESEC in the EGT area increased from 1.89×108t to 2.617×108 t; (2) the economic output effect is the main factor influencing carbon emissions in the EGT area, followed by population effect and energy structure effect, while energy intensity effect mitigates carbon emissions; and (3) CESEC will peak at 12.362×108t in 2030, leaving an arduous task on carbon reduction. The two-dimensional decoupling condition between carbon emissions and economic growth in the EGT area is low level-weak decoupling (WD-LE) for 2005-2019. The decoupling condition in Yulin and Ningdong is concentrated in low level-expansion connection (EC-LE) and low level-weak decoupling (WD-LE). Furthermore, Erdos reached high level-expansion negative decoupling (END-HE) condition during 2015-2019. Based on the above findings, a low-carbon development strategy for EGT should consider improving emission reduction technologies for high-carbon energy sources like coal, adjusting the energy consumption structure and seeking government policy support for carbon reduction.

Application of GM(1,1) model modified with seasonal factors in prediction of PM2.5 concentration in Shanghai / 公共卫生与预防医学

Objective To explore PM2.5 concentration modeling and prediction based on the monthly average concentrations of PM2.5 in Shanghai since 2015, and to provide new ideas about PM2.5 prediction methods. Methods The seasonal factors were introduced into the Grey Model (GM). GM(1,1) model modified with seasonal factors was established and compared with seasonal autoregressive integrated moving average model (ARIMA) model. The data of 2015-2021 was used for modeling and prediction, and the data from January to October in 2022 was used as a validation set to evaluate the prediction effectiveness. The monthly average PM2.5 concentrations in Shanghai from November to December in 2022 were predicted. Results Seasonal ARIMA model showed RMSE=4.02 and MAPE=15.50% in the validation set, while GM(1,1) model modified with seasonal factors showed RMSE=3.30 and MAPE=11.59%. GM(1,1) model modified with seasonal factors predicted the monthly average PM2.5 concentrations in Shanghai from November to December in 2022 to be 24.99 and 34.83μg/m3, respectively. Conclusion The prediction effect of GM(1,1) model modified with seasonal factors has better predictive performance than seasonal ARIMA model. The grey prediction model modified with seasonal factors can be considered when predicting seasonal time series such as the concentration of PM2.5.

Evaluation and Prediction of Ecological Sustainability in the Upper Reaches of the Yellow River Based on Improved Three-Dimensional Ecological Footprint Model.

Ecological footprint is an important method for regional sustainable assessment. Scientific assessment of the ecological sustainability of the upper reaches of the Yellow River is of great significance to the realization of a win-win situation for the ecological environment protection and economic development of the entire Yellow River basin. Based on the improved three-dimensional ecological footprint model, this paper measures and spatially portrays the ecological footprint per capita depth (EFdepth), ecological footprint per capita size (EFsize), and ecological footprint per capita 3D (per capita EF3D) of the upper Yellow River region from 2011 to 2020. Then, the ecological footprint diversity index (EFDI), integrated land stress index (Icomprehensive), ecological stress index (ETI), and ecological coordination coefficient (ECC) are used to evaluate its ecological safety and sustainability. The results of the study indicate that: (1) From 2011 to 2020, the three-dimensional ecological footprint of all provinces and regions in the upper reaches of the Yellow River was in a fluctuating upward trend as a whole, and NMG had the highest growth, from 2.6256 hm2/person to 3.3163 hm2/person, with an average annual growth rate of 2.36%. (2) In the past 10 years, the ETI index of the upper reaches of the Yellow River increased from 2.13 in 2011 to 3.28 in 2020, which is a serious insecurity. The EFDI index fluctuates slightly, but increases year by year. (3) The capital flow occupancy rate of the upper reaches of the Yellow River has been above 86.67%, and fluctuated during the study period, reaching a peak of 88.61% in 2020. (4) In the four periods, the number of land comprehensive pressure states and ecological security pressure states of the provinces and regions in the upper reaches of the Yellow River show a distribution pattern that the northeast region is better than the southwest region. This study is expected to provide scientific reference for land use in the upper reaches of the Yellow River, building the ecological security barrier of the Qinghai Tibet Plateau, and promoting sustainable socio-economic development.

Application of Grey Lotka-Volterra Model in Water-Economy-Industry-Technology Innovation System in Beijing-Tianjin-Hebei Region.

With the proposal of China's high-quality development strategy, how to promote regional stability and coordinated development based on a deep understanding of the main contradictions and changes in China's society has become the focus of research. High-quality development is a brand-new coordinated development concept, which aims to optimize the economic structure, transform the development model, enhance the development momentum, and take innovation as the primary driving force. How to promote the coordinated development of this region has become a hot issue considered by scholars. The Beijing-Tianjin-Hebei region is the capital economic circle of China, and the purpose of this study is to promote the coordinated and stable development of the region. On this premise, this paper firstly adopts the composite Grey Lotka-Volterra (GLV) model and Fractional GM(1,1) (FGM(1,1)) model to research Water Resources system-Economic System-Industrial System-Technology Innovation System in the Beijing-Tianjin-Hebei region. Secondly, by analyzing the research data, it is found that the relationship between the system is very complex, and the stability calculation results are all below 0. Then, the analysis of the research results shows that there is no obvious coordination among the three regions, and they have not yet reached a state of mutual promotion and stable and coordinated development. Finally, four suggestions are put forward for the coordinated development of the Beijing-Tianjin-Hebei region. This can not only provide direction for the future development of the region but also have reference significance for the development of other regions. Further, accelerate the coordination and unity of all factors of production in China and promote China's development at a deeper and higher level.

Impact of COVID-19 pandemic on the epidemiology of STDs in China: based on the GM (1,1) model.

BACKGROUND: COVID-19 and Sexually Transmitted Diseases (STDs) are two very important diseases. However, relevant researches about how COVID-19 pandemic has impacted on the epidemiological trend of STDs are limited in China. This study aimed to analyze the impact of COVID-19 on STDs in China and proposed relevant recommendations to be used in bettering health. METHODS: The incidence of HIV infection, syphilis and gonorrhea in China from 2008 to 2020 were collected. Grey Model (1,1) were established to predict the incidence of STDs with the incidence data of these three STDs from 2013 to 2018 considering the impact of policies in China, respectively. We then calculated the predictive incidence of each STD in 2019, 2020 and 2021 by the established Model. And we estimated the extent of the impact of COVID-19 on the epidemiological changes of STDs by analyzing the difference between the absolute percentage error (APE) of the predictive incidence and actual rate in 2019 and 2020. RESULTS: The incidence of HIV infection and syphilis showed a trend of increase from 2008 to 2019 in China, but that for gonorrhea was fluctuant. Of note, the incidence of these three STDs decreased significantly in 2020 compared with that in 2019. The APE of HIV infection, syphilis and gonorrhea in 2020 (20.54%, 15.45% and 60.88%) were about 7 times, 4 times and 2 times of that in 2019 (2.94%, 4.07% and 30.41%). The incidence of HIV infection, syphilis and gonorrhea would be 5.77/100,000, 39.64/100,000 and 13.19/100,000 in 2021 based on our model. CONCLUSIONS: The epidemiological trend of STDs in China was significant influenced by COVID-19 pandemic. It is important to balance the control of COVID-19 and timely management of STDs during the COVID-19 epidemic to prevent or reduce the poor outcome among COVID-19 patients with STDs. New management strategies on STDs, such as leveraging social media, online medical care, rapid self-testing, timely diagnosis and treatment guarantee and balance of medical resources for STDs management should be adapted in the context of the long-term effects of COVID-19.

Long short-term memory neural network and improved particle swarm optimization-based modeling and scenario analysis for municipal solid waste generation in Shanghai, China.

Accurate estimations of municipal solid waste (MSW) generation are vital to effective MSW management systems. While various single-point estimation approaches have been developed, the non-linearity and multiple site-specific influencing factors associated with MSW management systems make it challenging to forecast MSW generation quantities precisely. To address these concerns, this study developed a two-stage modeling and scenario analysis procedure for MSW generation and taking Shanghai as a test case demonstrated its viability. In the first stage, nine influencing factors were selected, and a hybrid novel forecasting model based on a long short-term memory neural network and an improved particle swarm optimization (IPSO-LSTM) was proposed for the forecasting of the MSW generation quantities, after which actual Shanghai data from 1980 to 2019 were used to test the performance. In the second stage, the future influencing variable values in different scenarios were predicted using an improved grey model, after which the predicted Shanghai MSW generation quantities from 2025 to 2035 were evaluated under various scenarios. It was found that (1) the proposed IPSO-LSTM had higher accuracy than the benchmark models; (2) the MSW generation quantities are expected to respectively increase to 9.971, 9.684, and 9.090 million tons by 2025 and 11.402, 11.285, and 10.240 by 2035 under the low, benchmark, and high scenarios; and (3) the MSW generation differences between the high and medium scenarios were decreasing.

Epidemiological analysis of varicella in Dalian from 2009 to 2019 and application of three kinds of model in prediction prevalence of varicella.

OBJECTIVE: This study described the epidemic characteristics of varicella in Dalian from 2009 to 2019, explored the fitting effect of Grey model first-order one variable( GM(1,1)), Markov model, and GM(1,1)-Markov model on varicella data, and found the best fitting method for this type of data, to better predict the incidence trend. METHODS: For this Cross-sectional study, this article was completed in 2020, and the data collection is up to 2019. Due to the global epidemic, the infectious disease data of Dalian in 2020 itself does not conform to the normal changes of varicella and is not included. The epidemiological characteristics of varicella from 2009 to 2019 were analyzed by epidemiological descriptive methods. Using the varicella prevalence data from 2009 to 2018, predicted 2019 and compared with actual value. First made GM (1,1) prediction and Markov prediction. Then according to the relative error of the GM (1,1), made GM (1,1)-Markov prediction. RESULTS: This study collected 37,223 cases from China Information System for Disease Control and Prevention's "Disease Prevention and Control Information System" and the cumulative population was 73,618,235 from 2009 to 2019. The average annual prevalence was 50.56/100000. Varicella occurred all year round, it had a bimodal distribution. The number of cases had two peaks from April to June and November to January of the following year. The ratio of males to females was 1.17:1. The 4 to 25 accounted for 60.36% of the total population. The age of varicella appeared to shift backward. Students, kindergarten children, scattered children accounted for about 64% of all cases. The GM(1,1) model prediction result of 2019 would be 53.64, the relative error would be 14.42%, the Markov prediction result would be 56.21, the relative error would be 10.33%, and the Gray(1,1)-Markov prediction result would be 59.51. The relative error would be 5.06%. CONCLUSIONS: Varicella data had its unique development characteristics. The accuracy of GM (1,1)-Markov model is higher than GM(1.1) model and Markov model. The model can be used for prediction and decision guidance.

Application of improved seasonal GM(1,1) model based on HP filter for runoff prediction in Xiangjiang River.

Runoff forecasting is essential for the reasonable use of regional water resources, flood prevention, and mitigation, as well as the development of ecological civilization. Runoff is influenced by the intersection of many factors, and the change process is extremely complex, showing significant stochasticity, nonlinearity, and heterogeneity, making traditional prediction models less adaptable. The Hodrick-Prescott filter (HP filter) is a well-established signal separation method. The traditional GM(1,1) model is capable of portraying the growth trend of the time series but cannot capture the periodic characteristics of the time series. Therefore, a novel coupled prediction model-HPF-GM(1,1) model is proposed in this study and applied to the runoff prediction of the Zhuzhou section of Xiangjiang River in Hunan Province. This model enables to separate seasonal factors from non-seasonal factors in the runoff time series, and introduce seasonal factors based on the traditional GM(1,1) model, which solves the challenge that the traditional GM(1,1) model is unable to predict seasonal time series. The results show that the HPF-GM(1,1) model has a mean relative error of 4.82%, a root mean square error of 7.44, and a Nash efficiency coefficient of 0.93, which is better than the traditional GM(1,1) model, the DGGM(1,1) model and the SGM(1,1) model of prediction accuracy. Obviously, the HP filter provides a new approach to data pre-processing of runoff series and the proposed HPF-GM(1,1)-coupled model extends new ideas for nonlinear runoff prediction.

Research on the Pathway of Green Financial System to Implement the Realization of China's Carbon Neutrality Target.

To answer to global climate change, promote climate governance and map out a grand blueprint for sustainable development, carbon neutrality has become the target and vision of all countries. Green finance is a means to coordinate economic development and environmental governance. This paper mainly studies the trend of carbon emission reduction in China in the next 40 years under the influence of green finance development and how to develop and improve China's green finance system to help China achieve the goal of "carbon neutrality by 2060". The research process and conclusions are as follows: (1) Through correlation test and data analysis, it is concluded that the development of green finance is an important driving force to achieve carbon neutrality. (2) The grey prediction GM (1,1) model is used to forecast the data of carbon dioxide emissions, green credit balance, green bond issuance scale and green project investment in China from 2020 to 2060. The results show that they will all increase year by year in the next 40 years. (3) BP neural network model is used to further predict carbon dioxide emissions from 2020 to 2060. It is expected that China's CO2 emissions will show an "inverted V" trend in the next 40 years, and China is expected to achieve a carbon peak in 2032 and be carbon neutral in 2063. Based on the results of the research above, this paper provides a supported path of implementing the realization of the carbon-neutral target of China from the perspective of developing and improving green financial system, aiming to provide references for China to realize the vision of carbon neutrality, providing policy suggestions for relevant departments, and provide ideas for other countries to accelerate the realization of carbon neutrality.

Yard waste prediction from estimated municipal solid waste using the grey theory to achieve a zero-waste strategy.

Yard waste is one of the key components of municipal solid waste and can play a vital role in implementing zero-waste strategy to achieve sustainable municipal solid waste management. Therefore, the objective of this study is to predict yard waste generation using the grey theory from the predicted municipal solid waste generation. The proposed model is implemented using municipal solid waste generation data from the City of Winnipeg, Canada. To identify the generation factors that influence municipal solid waste generation and yard waste generation, a correlation analysis is performed among eight socio-economic factors and six climatic factors. The GM (1, 1) model is utilized to predict individual factors with overall MAPE values of 0.06%-10.39% for the in-sample data, while the multivariable GM (1, N) grey model is employed to forecast the quarterly level of municipal solid waste generation with overall MAPE values of 5.64%-7.54%. In this study, grey models predict quarterly yard waste generation from the predicted municipal solid waste generation values using only twelve historical data points. The results indicate that the grey model (based on the error matrices) performs better than the linear and nonlinear regression-based models. The outcome of this study will support the City of Winnipeg's sustainable planning for yard waste management in terms of budgeting, resource allocation, and estimating energy generation.

Death and prediction analysis of adult hematological malignancies in Shenzhen / 白血病·淋巴瘤

Objective:To investigate the characteristics of death, tendency and the prediction of Shenzhen residents with adult hematological malignancies from 2017 to 2020.Methods:The surveillance data of hematological malignancies from 2017 to 2020 and the demographic data in Shenzhen were collected from Shenzhen death cause monitoring system and Shenzhen Center for Disease Control and Prevention, respectively. The data of the 7th national demographic data in 2020 were set as the standardized population data. Crude mortality rate (CMR), standardized mortality rate (SMR) and annual percentage change (APC) of mortality were calculated by using Joinpoint software. The grey model GM(1,1) was built to predict the mortality of adult hematological malignancies in Shenzhen between 2021 and 2025.Results:From 2017 to 2022, the male CMR of hematological malignancies was 1.15/100 000 to 1.85/100 000, and the SMR was 2.24/100 000 to 2.44/100 000; the female CMR of hematological malignancies was 0.81/100 000 to 1.75/100 000, and the SMR was 1.67/100 000 to 1.90/100 000. There were no statistically significant differences in the annual CMR and SMR between male and female hematological malignancies (all P > 0.05), and the annual change trend of CMR and SMR was not significant. The APC of male and female CMR was 27.28% and 12.70%, respectively (χ 2 = 0.01, P = 0.939); the APC of male and female SMR was 1.12% and 4.77%, respectively (χ 2 = 0.91, P = 0.318). The death causes of hematological malignancies were successively acute myeloid leukemia (AML), lymphoma, multiple myeloma, acute lymphoblastic leukemia (ALL), myelodysplastic syndrome (MDS) plus chronic myelomonocytic leukemia (CMML), chronic lymphoblastic leukemia (CLL) plus chronic myelogenous leukemia (CML). The CMR of patients with hematological malignancies aged 18-40 years was low, the CMR began to rise in patients above 40 years, especially the rapid increase at the age of 60 years, reaching the peak at the age of 80 years or above. The shortest median time of all kinds of hematological malignancies from the onset of disease to the death was found in AML group (8 months, range 0.1-168 months), the longest time was in CLL+CML group (24 months, range 0.1-300 months). Infection was the most direct cause of death, followed by single organ failure. GM(1,1) model had the better predictive effects and the total SMR would increase from 2021 to 2025 (4.52/100 000, 4.76/100 000, 5.01/100 000, 5.28/100 000 and 5.57/100 000, respectively). Conclusions:The incidence of hematological malignancies in Shenzhen residents over 40 years old is on the increase. The trend of adult hematological malignancies in Shenzhen will rise predicted by GM (1,1) grey model.

Research on Damage and Deterioration of Fiber Concrete under Acid Rain Environment Based on GM(1,1)-Markov.

With steel fiber and basalt fiber volume dosing serving as variation parameters, a total of 200 d cycles of acid rain corrosion cycle tests were conducted on fiber concrete in this study. We selected three durability evaluation parameters to assess the degree of damage deterioration on fiber concrete, used scanning electron microscopy, mercury intrusion porosimetry, and a dimensional microhardness meter to analyze the concrete micromorphology, and established a GM(1,1)-Markov model for life prediction of its durability. Results reveal that the acid rain environment is the most sensitive to the influence of the relative dynamic elastic modulus evaluation parameter, and concrete has specimens that show failure damage under this parameter evaluation. Incorporation of fibers can reduce the amount of corrosion products inside the concrete, decrease the proportion of harmful pores, optimize the mean pore-size, and significantly improve the resistance to acid rain attack. Concrete with 2% steel fiber and 0.1% basalt fiber by volume has the least change in durability damage, and the predicted service life by GM(1,1)-Markov model is 322 d.

The degree of equity and coupling coordination of staff in primary medical and health care institutions in China 2013-2019.

BACKGROUND: Primary medical and health care facilities are the first lines of defense for the health of population. This study aims to evaluate the current state and trend of equity and coupling coordination degree (CCD) of staff in primary medical and health care institutions (SPMHCI) based on the quantity and living standards of citizens in China 2013-2019. The research findings are expected to serve as a guideline for the allocation of SPMHCI. METHODS: The data used in this study including the quantity and living standards of citizens, as well as the number of SPMHCI in 31 provincial administrative regions of China, were obtained from the China Statistical Yearbook and the China Health Statistics Yearbook. The equity and CCD for SPMHCI were analyzed by using the Gini coefficient and the CCD model, and the Grey forecasting model GM (1, 1) (GM) was used to predict the equity and CCD from 2020 to 2022. RESULTS: Between 2013 and 2019, the number of SPMHCI increased from 3.17 million to 3.50 million, and the population-based Gini coefficient declined from 0.0704 to 0.0513. In urban and rural areas, the Gini coefficients decreased from 0.1185 and 0.0737 to 0.1025 and 0.0611, respectively. The CCD between SPMHCI and citizens' living standards (CLS) changed from 0.5691, 0.5813, 0.5818 to 0.5650, 0.5634, 0.6088 at national, urban, and rural levels, respectively. The forecasting results of GM revealed that at the national, urban and rural levels from 2020 to 2022, the Gini coefficient would rise at a rate of - 13.53, - 5.77%, and - 6.10%, respectively, while the CCD would grow at a rate of - 0.89, 1.06, and 0.87%, respectively. CONCLUSIONS: In China, the number of SPMHCI has increased significantly, with an equitable allocation based on the population. The interaction between SPMHCI and CLS is sufficient, but the degree of mutual promotion is moderate. The government could optimize SPMHCI and improve the chronic disease management services to improve CLS and to ensure the continued operation of primary medical and health care institutions in urban areas.