Study on the burden of digestive diseases among Chinese residents in the 21st century.

Background: The global burden of digestive diseases has increased in recent years. The study aims to comprehend the trend of incidence and death rates related to digestive diseases in China from 2000 to 2020. Methods: The study collected data on digestive diseases and their causes, such as incidence rates, death rates, Years of Life Lost, Years Lived with Disability, Disability-Adjusted Life Years and estimated annual percentage change from the 2019 Global Burden of Disease website and the Chinese Health and Wellness Statistical Yearbook spanning. And we employed the age-period-cohort model to analyze the influence of age, period, and birth cohort on the trend of death rates associated with digestive diseases. Results: In contrast to the global burden of digestive disease, China experienced increases in the age-standardized incidence for inflammatory bowel disease, gallbladder and biliary diseases, as well as appendicitis from 2000 to 2019. The corresponding estimated annual percentage change for these diseases were 2.06, 1.74, and 0.99. Females showed a significantly higher incidence of digestive diseases, while males experienced a higher death rate. Moreover, individuals in the age groups under 5 years and over 60 years exhibited higher death rates than those in other age groups. Conclusion: The findings underscore the ongoing importance of digestive diseases as a substantial public health issue in China. Reducing the disease burden of IBD in China necessitates healthcare systems to enhance their infrastructure and personnel readiness, ensuring an equitable, affordable, and accessible distribution of care for IBD patients. To reduce the occurrence and mortality rates of digestive diseases in China, the government should promote the adoption of early screening policies for individuals under the 5 year and those above the 60 year. These policies should be accompanied by customized preventive measures.

Establishment and application of risk classification model for lead in vegetables based on spectral clustering algorithms.

This study aims to evaluate the risk of lead pollution in 9 kinds of vegetables consumed by residents in 20 provinces/cities of China. Sampling data and vegetable consumption data from 20 provinces/cities in 2019 were used. Combined with dietary exposure assessment, the vegetable categories and provinces were paired, and a risk classification model based on spectral clustering algorithms was proposed. The results of the spectral clustering algorithm showed that the risk level of lead pollution in vegetables can be divided into five levels. The combination of vegetable-province/cities at the risk level of 1 and 2 accounted for 92.78%, and that at the risk level of 4 and 5 accounted for 2.22%. The high-risk combinations were fresh edible fungus-Shaanxi, fresh edible fungus-Sichuan, and fresh edible fungus-Shanghai and bean sprouts-Guangdong. In the proposed model, objective data were used as the classification index, and the spectral clustering algorithm was employed to select the optimal risk classification in a data-driven way. As a result, the influence of subjective factors was effectively reduced, the risk of lead pollution in vegetables was classified, and the results were scientific and accurate. This study provides a scientific basis of supervision priorities for regulatory departments.

A logistic model and predictions for the spread of the COVID-19 pandemic.

The rapid spread of COVID-19 worldwide presents a great challenge to epidemic modelers. Model outcomes vary widely depending on the characteristics of a pathogen and the models. Here, we present a logistic model for the epidemic spread and divide the spread of the novel coronavirus into two phases: the first phase is a natural exponential growth phase that occurs in the absence of intervention and the second phase is a regulated growth phase that is affected by enforcing social distancing and isolation. We apply the model to a number of pandemic centers. Our results are in good agreement with the data to date and show that social distancing significantly reduces the epidemic spread and flattens the curve. Predictions on the spreading trajectory including the total infections and peak time of new infections for a community of any size are made weeks ahead, providing the vital information and lead time needed to prepare for and mitigate the epidemic. The methodology presented here has immediate and far-reaching applications for ongoing outbreaks or similar future outbreaks of other emergent infectious diseases.

Scaling laws for ignition at the National Ignition Facility from first principles.

We have developed an analytical physics model from fundamental physics principles and used the reduced one-dimensional model to derive a thermonuclear ignition criterion and implosion energy scaling laws applicable to inertial confinement fusion capsules. The scaling laws relate the fuel pressure and the minimum implosion energy required for ignition to the peak implosion velocity and the equation of state of the pusher and the hot fuel. When a specific low-entropy adiabat path is used for the cold fuel, our scaling laws recover the ignition threshold factor dependence on the implosion velocity, but when a high-entropy adiabat path is chosen, the model agrees with recent measurements.

Buoyancy-drag mix model obtained by multifluid interpenetration equations.

In this paper, a buoyancy drag equation for describing the motion of the edges of a mixing zone driven by Rayleigh-Taylor or Richtmyer-Meshkov instabilities is derived from the multifluid interpenetration mix model equations of Scannapieco and Cheng [Phys. Letters A 299, 49 (2002)]. This derivation provides a physics foundation for a large class of phenomenological buoyancy-drag mix models and also establishes a physical connection between the microscopic collision frequency and the macroscopic fluid drag coefficient. The predicted values for model parameter a(ss') in the multifluid interpenetration mix model, from the Rocket-Rig experiments, is in the range of 0.043-0.125 depending upon the Atwood number. The results are also in good agreement with inertial confinement fusion capsule implosions.

Dynamical evolution of Rayleigh-Taylor and Richtmyer-Meshkov mixing fronts.

Dynamic behavior of mixing fronts plays a crucial role in multifluid turbulent mixing. In this paper, we derive an analytic solution for the dynamic evolution of mixing fronts driven by constant acceleration Rayleigh-Taylor (RT) and impulsive acceleration Richtmyer-Meshkov instabilities, from a simple physics model expressed as a pair of ordinary differential equations. An approximate closed form asymptotic evaluation of the RT solution is obtained, through terms of order O(1), as t--> infinity. This three term expansion, including lower order terms, is used to interpret experimental and simulation data. Our solutions improve on previous analyses in their agreement with experimental data, in that we can fit both the slope and the intercept of the Z(b) vs Agt(2) experimental plots by adjusting parameters in our model. Since the experimental data are close to self-similar, the improvement due to the lower order contributions in the asymptotic expansion is modest. We also apply this analysis to simulation data, for which preasymptotic data exist. We reexamine previous simulation data and determine an improved growth rate alpha(b)=0.0625. The present paper provides concepts and tools to explore the preasymptotic aspects of these data.

A three-dimensional renormalization group bubble merger model for Rayleigh-Taylor mixing.

In this paper we formulate a model for the merger of bubbles at the edge of an unstable acceleration driven (Rayleigh-Taylor) mixing layer. Steady acceleration defines a self-similar mixing process, with a time-dependent inverse cascade of structures of increasing size. The time evolution is itself a renormalization group (RNG) evolution, and so the large time asymptotics define a RNG fixed point. We solve the model introduced here at this fixed point. The model predicts the growth rate of a Rayleigh-Taylor chaotic fluid mixing layer. The model has three main components: the velocity of a single bubble in this unstable flow regime, an envelope velocity, which describes collective excitations in the mixing region, and a merger process, which drives an inverse cascade, with a steady increase of bubble size. The present model differs from an earlier two-dimensional (2-D) merger model in several important ways. Beyond the extension of the model to three dimensions, the present model contains one phenomenological parameter, the variance of the bubble radii at fixed time. The model also predicts several experimental numbers: the bubble mixing rate, alpha(b)=h(b)/Agt(2) approximately 0.05-0.06, the mean bubble radius, and the bubble height separation at the time of merger. From these we also obtain the bubble height to the radius aspect ratio. Using the experimental results of Smeeton and Youngs (AWE Report No. O 35/87, 1987) to fix a value for the radius variance, we determine alpha(b) within the range of experimental uncertainty. We also obtain the experimental values for the bubble height to width aspect ratio in agreement with experimental values. (c) 2002 American Institute of Physics.