Time series analysis-based seasonal autoregressive fractionally integrated moving average to estimate hepatitis B and C epidemics in China

BACKGROUND Hepatitis B(HB)and hepatitis C(HC)place the largest burden in China,and a goal of eliminating them as a major public health threat by 2030 has been set.Making more informed and accurate forecasts of their spread is essential for developing effective strategies,heightening the requirement for early warning to deal with such a major public health threat.AIM To monitor HB and HC epidemics by the design of a paradigmatic seasonal autoregressive fractionally integrated moving average(SARFIMA)for projections into 2030,and to compare the effectiveness with the seasonal autoregressive integrated moving average(SARIMA).METHODS Monthly HB and HC incidence cases in China were obtained from January 2004 to June 2023.Descriptive analysis and the Hodrick-Prescott method were employed to identify trends and seasonality.Two periods(from January 2004 to June 2022 and from January 2004 to December 2015,respectively)were used as the training sets to develop both models,while the remaining periods served as the test sets to evaluate the forecasting accuracy.RESULTS There were incidents of 23400874 HB cases and 3590867 HC cases from January 2004 to June 2023.Overall,HB remained steady[average annual percentage change(AAPC)=0.44,95%confidence interval(95%CI):-0.94-1.84]while HC was increasing(AAPC=8.91,95%CI:6.98-10.88),and both had a peak in March and a trough in February.In the 12-step-ahead HB forecast,the mean absolute deviation(15211.94),root mean square error(18762.94),mean absolute percentage error(0.17),mean error rate(0.15),and root mean square percentage error(0.25)under the best SARFIMA(3,0,0)(0,0.449,2)12 were smaller than those under the best SARIMA(3,0,0)(0,1,2)12(16867.71,20775.12,0.19,0.17,and 0.27,respectively).Similar results were also observed for the 90-step-ahead HB,12-step-ahead HC,and 90-step-ahead HC forecasts.The predicted HB incidents totaled 9865400(95%CI:7508093-12222709)cases and HC totaled 1659485(95%CI:856681-2462290)cases during 2023-2030.CONCLUSION Under current interventions,China faces enormous challenges to eliminate HB and HC epidemics by 2030,and effective strategies must be reinforced.The integration of SARFIMA into public health for the management of HB and HC epidemics can potentially result in more informed and efficient interventions,surpassing the capabilities of SARIMA.

Multi-Criteria Group Decision-Making Method Based on an Improved Single-Valued Neutrosophic Hamacher Weighted Average Operator and Grey Relational Analysis

This paper proposes a multi-criteria decision-making (MCGDM) method based on the improved single-valued neutrosophic Hamacher weighted averaging (ISNHWA) operator and grey relational analysis (GRA) to overcome the limitations of present methods based on aggregation operators. First, the limitations of several existing single-valued neutrosophic weighted averaging aggregation operators (i.e. , the single-valued neutrosophic weighted averaging, single-valued neutrosophic weighted algebraic averaging, single-valued neutrosophic weighted Einstein averaging, single-valued neutrosophic Frank weighted averaging, and single-valued neutrosophic Hamacher weighted averaging operators), which can produce some indeterminate terms in the aggregation process, are discussed. Second, an ISNHWA operator was developed to overcome the limitations of existing operators. Third, the properties of the proposed operator, including idempotency, boundedness, monotonicity, and commutativity, were analyzed. Application examples confirmed that the ISNHWA operator and the proposed MCGDM method are rational and effective. The proposed improved ISNHWA operator and MCGDM method can overcome the indeterminate results in some special cases in existing single-valued neutrosophic weighted averaging aggregation operators and MCGDM methods.

Examination of the profitability of technical analysis based on moving average strategies in BRICS

In this paper,we investigated the profitability of technical analysis as applied to the stock markets of the BRICS member nations.In addition,we searched for evidence that technical analysis and fundamental analysis can complement each other in these markets.To implement this research,we created a comprehensive portfolio containing the assets traded in the markets of each BRICS member.We developed an automated trading system that simulated transactions in this portfolio using technical analysis techniques.Our assessment updated the findings of previous research by including more recent data and adding South Africa,the latest member included in BRICS.Our results showed that the returns obtained by the automated system,on average,exceeded the value invested.There were groups of assets from each country that performed well above the portfolio average,surpassing the returns obtained using a buy and hold strategy.The returns from the sample portfolio were very strong in Russia and India.We also found that technical analysis can help fundamental analysis identify the most dynamic companies in the stock market.

Average Incremental Correlation Analysis Model and Its Application in Fault Diagnosis

The concept of average incremental correlation degree is put forward.It has been proved that the average incremental correlation model has such properties as parallelism,consistency,affine,affine transformation isotonicity and interference factors independence,and it will not lead to changes of the sequence order relation because of the data transformation.Therefore,the new model keeps good stability.Finally,the incremental average correlation model is applied to failure model analysis of equipment,and an ideal diagnostic effect is obtained.

Integrating image processing and deep learning for effective analysis and classification of dust pollution in mining processes

A comprehensive evaluation method is proposed to analyze dust pollution generated in the production process of mines.The method employs an optimized image-processing and deep learning framework to characterize the gray and fractal features in dust images.The research reveals both linear and logarithmic correlations between the gray features,fractal dimension,and dust mass,while employing Chauvenel criteria and arithmetic averaging to minimize data discreteness.An integrated hazardous index is developed,including a logarithmic correlation between the index and dust mass,and a four-category dataset is subsequently prepared for the deep learning framework.Based on the range of the hazardous index,the dust images are divided into four categories.Subsequently,a dust risk classifcation system is established using the deep learning model,which exhibits a high degree of performance after the training process.Notably,the model achieves a testing accuracy of 95.3%,indicating its efectiveness in classifying diferent levels of dust pollution,and the precision,recall,and F1-score of the system confrm its reliability in analyzing dust pollution.Overall,the proposed method provides a reliable and efcient way to monitor and analyze dust pollution in mines.

Applications of time series analysis in epidemiology: Literature review and our experience during COVID-19 pandemic

Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways:Prediction and forecast.Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role.Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences.The time series analysis approach has the advantage of being easier to use(in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average).Still,it is limited in forecasting time,unlike the classical models such as Susceptible-Exposed-Infectious-Removed.Its applicability in forecasting comes from its better accuracy for short-term prediction.In its basic form,it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures(governments,companies,etc.).Instead,it estimates from the data directly.Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread;be it school closures,emerging variants,etc.It can be used in mortality or hospital risk estimation from new cases,seroprevalence studies,assessing properties of emerging variants,and estimating excess mortality and its relationship with a pandemic.

Data envelopment analysis procedure with two non-homogeneous DMU groups

The classic data envelopment analysis（DEA） model is used to evaluate decision-making units＇（DMUs） efficiency under the assumption that all DMUs are evaluated with the same criteria setting. Recently, new researches begin to focus on the efficiency analysis of non-homogeneous DMU arose by real practices such as the evaluation of departments in a university, where departments argue for the adoption of different criteria based on their disciplinary characteristics. A DEA procedure is proposed in this paper to address the efficiency analysis of two non-homogeneous DMU groups. Firstly, an analytical framework is established to compromise diversified input and output（IO） criteria from two nonhomogenous groups. Then, a criteria fusion operation is designed to obtain different DEA analysis strategies. Meanwhile, Friedman test is introduced to analyze the consistency of all efficiency results produced by different strategies. Next, ordered weighted averaging（OWA） operators are applied to integrate different information to reach final conclusions. Finally, a numerical example is used to illustrate the proposed method. The result indicates that the proposed method relaxes the restriction of the classical DEA model,and can provide more analytical flexibility to address different decision analysis scenarios arose from practical applications.

Land use suitability analysis for town development planning in Nanjing hilly areas: A case study of Tangshan new town, China

Land use suitability analysis plays an important role in sustainable land use and solving environmental problems caused by rapid urban development.A land use suitability mapping approach for town development planning in hilly areas was constructed based on two multi-criteria evaluation methods:Weighted Linear Combination(WLC)and Ordered Weighted Averaging(OWA),to comparatively evaluate and map land use suitability of Tangshan new town in Nanjing,China.Fourteen evaluation factors related to topographic,environmental,socio-economics and historical sites data were used as suitability criteria.The analytic hierarchy process(AHP)method and GIS techniques were integrated into the evaluation models to create the land use suitability map for town development planning.The results of WLC approach showed that 11.4%of the total area is highly suitable while the 48.6%is unsuitable.The results of WLC and OWA approach showed the distribution of degree of land use suitability is almost the same.The areas located at the southern and eastern flat regions are highly suitable for land use,whereas the areas close to the mountain forests,steep slopes,waters,and hot springs,have lower suitability for land use.Sensitivity analysis indicated that the suitability results of the two proposed methods are robust.Indirect validation was achieved by mutual comparison of suitability maps derived from the WLC and OWA methods.It demonstrated that the overall agreement is 90.81%and kappa coefficient is 0.81,indicating that both methods provide very similar spatial suitability distributions.By overlaying the resultant map with the previous master plan map of Tangshan new town,the overlay map once again indicated a satisfactory ecological fit between the two maps.At last,several recommendations are proposed aiming at improving the long-term town development plan for Tangshan new town.

Urban natural gas demand and factors analysis in China:Perspectives of price and income elasticities

Urban natural gas is becoming the main sector driving China’s natural gas consumption growth in recent years.This study explores the impacts of urban natural gas price,wage,socioeconomic determinants,and meteorological conditions on urban natural gas demand in China over 2006-2017.Furthermore,this study also analyzes the potential regional heterogeneity and asymmetry in the impacts of gas price and income on China’s urban gas demand.Empirical results reveal that:(1)The increased gas price can significantly reduce the urban gas demand,and the average income level may effectively promote the gas demand,also,a strong switching effect exists between electricity and natural gas in urban China;(2)these impacts are heterogeneous in regions among China,urban natural gas demand is largely affected by the gas price in regions with high-gas-price and by income in regions with low-gas-price;and(3)the impact of gas price on urban gas consumption is consistent in regions with different urban natural gas consumption,while the impact of income is asymmetric.This study further provides several policy implications for improving the urban natural gas industry in China.

Worldwide suicide mortality trends(2000-2019):A joinpoint regression analysis

BACKGROUND Studies exploring suicide mortality on a global scale are sparse,and most evaluations were limited to certain populations.AIM To assess global,regional and national trends of suicide mortality.METHODS Suicide mortality data for the period 2000-2019 were obtained from the mortality database of the World Health Organization and the Global Burden of Disease Study.Age-standardized rates(ASRs;expressed per 100000)were presented.To assess trends of suicide mortality,joinpoint regression analysis was used:The average annual percent change(AAPC)with the corresponding 95%confidence interval(95%CI)was calculated.RESULTS A total of 759028(523883 male and 235145 female)suicide deaths were reported worldwide in 2019.The global ASR of mortality of suicide was 9.0/100000 population in both sexes(12.6 in males vs 5.4 in females).In both sexes,the highest rates were found in the region of Africa(ASR=11.2),while the lowest rates were reported in Eastern Mediterranean(ASR=6.4).Globally,from 2000 to 2019,ASRs of mortality of suicide had a decreasing tendency in both sexes together[AAPC=-2.4%per year;95%CI:(-2.6)-(-2.3)].The region of the Americas experienced a significant increase in suicide mortality over 2000-2019 unlike other regions that had a declining trend.Out of all 133 countries with a decline in suicide mortality,Barbados(AAPC=-10.0%),Grenada(AAPC=-8.5%),Serbia(AAPC=-7.6%),and Venezuela(AAPC=-6.2%)showed the most marked reduction in mortality rates.Out of all 26 countries with a rise in suicide mortality,Lesotho(AAPC=+6.0%),Cyprus(AAPC=+5.1%),Paraguay(AAPC=+3.0%),Saudi Arabia(AAPC=+2.8%),Brunei(AAPC=+2.6%),Greece(AAPC=+2.6%),Georgia(AAPC=+2.1%),and Mexico(AAPC=+2.0%),are among those with the highest increase in mortality.CONCLUSION Decreasing trends in suicide mortality were observed in most countries across the world.Unfortunately,the mortality of suicide showed an increasing trend in a number of populations.Further research should explore the reasons for these unfavorable trends,in order to consider and recommend more efforts for suicide prevention in these countries.

The Correlation of Hospital Operational Efficiency and Average Length of Stay in China: A Study Based on Provincial Level Data

Objective: To measure the hospital operation efficiency, study the correlation between average length of stay and hospital operation efficiency, analyze the importance of shortening average length of stay to the improvement of the hospital operation efficiency and put forward relevant policy suggestion. Methods: Based on China provincial panel data from 2003 to 2012, the hospital operation efficiencies are calculated using Super Efficiency Data Envelopment Analysis model, and the correlation between average length of stay and hospital operation efficiency is tested using Spearman rank correlation coefficient test. Results: From 2003 to 2012, the average of national hospital operation efficiency was increasing slowly and the hospital operations were inefficient in most of the areas. The national hospital operation efficiency is negatively correlated to the average length of stay. Conclusion: Measures should be taken to set average length of stay in a scientific and reasonable way, improve social and economic benefits based on the improvement of efficiency.

Time series analysis of correlativity between pulmonary tuberculosis and seasonal meteorological factors based on theory of Human-Environmental Inter Relation

Objective:This paper aims to study the correlativity between the number of pulmonary tuberculosis (PTB) cases and seasonal meteorological factors in Beijing.Methods:Based on theory of Human-Environmental Inter Relation in Huangdi's Internal Classics,we adopted monthly cases of PTB in Beijing from 2004 to 2011,and established a Seasonal Autoregressive Integrated Moving Average (SARIMA) model.Using the cross-correlation function (CCF),we then analyzed the correlation between meteorological factors and number of infected patients.The related meteorological factors were subsequently integrated,to establish a Seasonal Autoregressive Integrated Moving Average with explanatory variables (SARIMAX) model,which was used to estimate and verify the number of PTB cases in 2012.Results:In this study,a SARIMA(0,1,1) (0,1,1)12 model was established;CCF analysis was used to reveal the correlativity between PTB and precipitation with 1 lag,relative humidity with 1 lag.Then,integrated with relative humidity with 1 lag (β =2.405,95% confidence interval:0.433-4.377),the SARIMAX prediction model was proved to be an accurate approach for predicting local situations of PTB occurrence.Conclusions:The occurrence of PTB is correlated with seasonal meteorological factors.Combining these factors,an exact prediction model can be established,to estimate of the number of PTB infected patients.

Impedance Modeling and Stability Analysis of Dual Active Bridge Converter with LC Input Filter

The dual active bridge(DAB)converter is gaining more and more attention in various applications such as energy storage systems,electric vehicles and smart grids.To improve the quality of the input current,a LC filter is often cascaded at the input side of the DAB converter.However,there are instable problems of this cascaded system due to the impedance interactions of the DAB converter and the LC filter,although the DAB converter is stable at the individual operation mode.To assess the stability of the cascaded system of the DAB converter and the LC filter,the impedance model of the DAB converter is firstly developed based on generalized state-space averaging method.The developed impedance model can be used to accurately predict the stability of the DAB converter with its LC input filter.Based on the stability analysis,the optimum filter parameter design guideline is determined.The impedance model and stability analysis are validated by the simulation and experimental results.

Statistical Analysis for the Driving Cycle of Beijing's Bus

According to the test data of the driving model for Beijing's bus routes, 9 parameters and the actual values of Beijing bus are confirmed to evaluate the driving cycle, 2 ways of establishing driving cycle model are analyzed, the formula of calculating driving cycle is acquired, and the calculating driving cycle model and the statistical driving cycle model for the buses in Beijing urban areas are set up. This study provides scientific basis for selecting the bus type and confirming the design parameters and the running method in Beijing.

Sufficient Condition for Average Consensus of Directed Networked Multi-agent Systems with Time Delays

The average consensus problem in a directed network of multi-agent systems with communication time delays was investigated. The directed networks were balanced and weakly connected with fixed or switching topology digraph. Based on frequency domain analysis method, a sufficient condition of asymptotic stability of multi-agent systems with time delays was obtained,where the analytic formula between the maximum time delay and the directed network structure was provided. The maximum time delay can be derived directly and easily by the eigenvalue of Laplacian L. Numerical examples confirm the effectiveness of the proposed technique.

Progressive failure analysis of composite structure based on micro- and macro-mechanics models

Based on parameter design language, a program of progressive failure analysis in composite structures is proposed. In this program, the relationship between macro- and micro-mechanics is established and the macro stress distribution of the composite structure is calculated by commercial finite element software. According to the macro-stress, the damaged point is found and the micro-stress distribution of representative volume element is calculated by finite-volume direct averaging micromechanics(FVDAM). Compared with the results calculated by failure criterion based on macro-stress field(the maximum stress criteria and Hashin criteria) and micro-stress field(Huang model), it is proven that the failure analysis based on macro- and micro-mechanics model is feasible and efficient.

Simulation of Spatial Distribution of Annual Average Precipitation in Shandong Province

Based on DEM data and conventional data of precipitation at 114 meteorological stations in Shandong Province during 1971 -2010, the statistical model of annual average precipitation in Shandong Province was established using SPSS software; DEM data and raster data of latitude and longitude were substituted into the statistical model, and the spatial distribution of annual average precipitation in Shandong Province based on the statistical model was obtained with the aid of ArcGIS software. Afterwards, the difference between actual value of precipitation at stations used in interpolation and simulated value was interpolated using Kriging interpolation method to obtain residual error of precipitation. Finally, the raster da- ta of annual average precipitation based on the regression model were overlaid with residual error of precipitation to obtain the spatial distribution map of annual average precipitation in Shandong Province. It is verified that the simulation result has high accuracy and can reflect the spatial distri- bution of precipitation in Shandong Province.

APL-based flexibility analysis of manufacturing grid

With the characteristics of diversity, randomness, concurrency and decomposability, tasks in manufacturing field are very complicated, and so manufacturing grid (MG) should have considerable flexibility to deal with this problem. With the definition of node and arc, MG structure is converted into a small-world network. Given construction cost constraint, the problem of shortest task waiting time is transformed into the constrained optimization problem, and a corresponding ?exibility analysis model based on average path length (APL) is proposed, and the premise of arc-length and node-distance are defined. The results of application example show that the analysis model is effiective.

Constructing refined null models for statistical analysis of signed networks

The establishment of effective null models can provide reference networks to accurately describe statistical properties of real-life signed networks.At present,two classical null models of signed networks(i.e.,sign and full-edge randomized models)shuffle both positive and negative topologies at the same time,so it is difficult to distinguish the effect on network topology of positive edges,negative edges,and the correlation between them.In this study,we construct three re-fined edge-randomized null models by only randomizing link relationships without changing positive and negative degree distributions.The results of nontrivial statistical indicators of signed networks,such as average degree connectivity and clustering coefficient,show that the position of positive edges has a stronger effect on positive-edge topology,while the signs of negative edges have a greater influence on negative-edge topology.For some specific statistics(e.g.,embeddedness),the results indicate that the proposed null models can more accurately describe real-life networks compared with the two existing ones,which can be selected to facilitate a better understanding of complex structures,functions,and dynamical behaviors on signed networks.