Study on the Spatiotemporal Pattern Evolution and Influencing Factors of Population Aging in Henan Province

This study utilized census data from Henan Province for the years 2000,2010,and 2020 to investigate the spatiotemporal evolution of population aging,defined by the proportion of the population aged 65 and above.Employing spatial analysis techniques such as spatial autocorrelation and the standard deviation ellipse,the research mapped out the progression and distribution of aging demographics.Furthermore,the study delved into the influencing factors of aging using an optimal parameters-based geographical detector.Results indicate a deepening degree of population aging in Henan Province,transitioning from an adult type to an old type structure.There is a marked positive spatial correlation among counties,with high-value aging areas initially decreasing,then increasing,and notably spreading from the central to the central and southern regions of the province.The center of gravity for population aging,specifically around Changge City and Xuchang City,exhibits a trajectory moving southeast before shifting northwest.Factor detection reveals that in 2000,2010,and 2020,the elderly dependency ratio predominantly influences the aging trend,with explanatory powers of 88.4%,87.9%,and 90.9%,respectively.Interaction analysis indicates that the interaction between the old-child ratio and the elderly dependency ratio has a strong explanatory power for the aging patterns in Henan Province,reaching 97.3%,97.0%,and 97.4%,respectively.

Placement Optimization Method of FPSO Gas Detectors Based on Leakage Risk

As the first step of the fire/gas-detection systems of floating production storage and offloading(FPSO)units is to iden-tify leakage accidents,gas detectors play an important role in controlling the leakage risk.To improve the leakage scenario detection rate and reduce the cumulative risk value,this paper presents an optimization method of the gas detector placement.The probability density distribution and cumulative probability density distribution of the leakage source variables and environmental variables were calculated based on the Offshore Reliability Data and the statistical data of the relevant leakage variables.A potential leakage sce-nario set was constructed using Latin hypercube sampling.The typical FPSO leakage scenarios were analyzed through computational fluid dynamics(CFD),and the impacts of different parameters on the leakage were addressed.A series of detectors was deployed according to the simulation results.The minimization of the product of effective detection time and gas leakage volume was the risk optimization objective,and the location and number of detectors were taken as decision variables.A greedy extraction heuristic algo-rithm was used to solve the optimization problem.The results show that the optimized placement had a better monitoring effect on the leakage scenario.

Analyzing the spatiotemporal evolution and driving forces of gross ecosystem product in the upper reaches of the Chaobai River Basin

The Chaobai River Basin,which is a crucial ecological barrier and primary water source area within the Beijing-Tianjin-Hebei region,possesses substantial ecological significance.The gross ecosystem product(GEP)in the Chaobai River Basin is a reflection of ecosystem conditions and quantifies nature’s contributions to humanity,which provides a basis for basin ecosystem service management and decision-making.This study investigated the spatiotemporal evolution of GEP in the upper Chaobai River Basin and explored the driving factors influencing GEP spatial differentiation.Ecosystem patterns from 2005 to 2020 were analyzed,and GEP was calculated for 2005,2010,2015,and 2020.The driving factors influencing GEP spatial differentiation were identified using the optimal parameter-based geographical detector(OPGD)model.The key findings are as follows:(1)From 2005 to 2020,the main ecosystem types were forest,grassland,and agriculture.Urban areas experienced significant changes,and conversions mainly occurred among urban,water,grassland and agricultural ecosystems.(2)Temporally,the GEP in the basin increased from 2005 to 2020,with regulation services dominating.At the county(district)scale,GEP exhibited a north-west-high and south-east-low pattern,showing spatial differences between per-unit-area GEP and county(district)GEP,while the spatial variations in per capita GEP and county(district)GEP were similar.(3)Differences in the spatial distribution of GEP were influenced by regional natural geographical and socioeconomic factors.Among these factors,gross domestic product,population density,and land-use degree density contributed significantly.Interactions among different driving forces noticeably impacted GEP spatial differentiation.These findings underscore the necessity of incorporating factors such as population density and the intensity of land-use development into ecosystem management decision-making processes in the upper reaches of the Chaobai River Basin.Future policies should be devised to regulate human activities,thereby ensuring the stability and enhancement of GEP.

An iterative refinement method combining detector geometry optimization and diffraction model refinement in serial femtosecond crystallography

Background Recent advances in serial femtosecond crystallography(SFX)using X-ray free electron lasers(XFELs)have facilitated accurate structure determination for biological macromolecules.However,given the many fluctuations inherent in SFX,the acquisition of SFX data of sufficiently high quality still remains challenging.Method Aimed at enhancing the accuracy of SFX data,this study proposes an iterative refinement method to optimally match pairs of the observed and predicted reflections on the detector plane.This method features a combination of detector geometry optimization and diffraction model refinement in an alternate manner,concomitant with a cycle-by-cycle peak selection procedure.Result To demonstrate whether this iterative method is convergent and feasible,both numerical simulations and experimental tests have been performed.The results reveal that this method can gradually improve overall quality of the integrated SFX data and therefore accelerate the convergence of Monte Carlo integration,while simultaneously suppressing correlations inherent in certain parameters and precluding outliers to some extent during the refinement.Conclusion We have demonstrated that our iterative refinement method is applicable to both simulated and experimental SFX data.It is expected that this method could provide meaningful insights into the refinement of SFX data and take the step forward toward more accurate Monte Carlo integration.