Additional file 3 of Global, regional, and national burden of hypertensive heart disease during 1990–2019: an analysis of the global burden of disease study 2019

Additional file 3: Table S1. The global and regional burden of hypertensive heart disease

Additional file 5 of Global, regional, and national burden of hypertensive heart disease during 1990–2019: an analysis of the global burden of disease study 2019

Additional file 5: Table S3. The global and regional age-standardized variation trends of hypertensive heart disease from 1990 to 2019

Interactive web mapping of geodemographics through user-specified regionalisations

The analysis of spatial distributions is possible using a broad spectrum of new and existing digital data sources. Challenges can arise with respect to use of areal units that are both appropriate and compatible. In addition, regional statistics are prone to scale and aggregation effects that manifest the modifiable areal unit problem (MAUP). This paper introduces a web mapping system that allows users to experiment with standard and bespoke zonal schemes in the geodemographic analysis of regional patterns. We describe the architecture and design of the platform and its associated data processing techniques before demonstrating its value through user case scenarios. Using the segregation index as an example, we demonstrate how the use of interactive maps can assist in revealing the scale-dependent nature of the index. Our web mapping system can be employed to help geography students, policymakers and researchers better understand the underlying geodemographic structure of functional regions.</p

Additional file 2 of Global, regional, and national burden of hypertensive heart disease during 1990–2019: an analysis of the global burden of disease study 2019

Additional file 2: Figure S2. The top 20 countries with high disease burden (A, number of prevalence; B, prevalence rates; C, age-standardized prevalence rates; D, number of death; E, death rates; F, age-standardized death rates)

Discovering Catalytic Reaction Networks Using Deep Reinforcement Learning from First-Principles

Determining the reaction pathways, which is central to illustrating the working mechanisms of a catalyst, is severely hindered by the high complexity of the reaction and the extreme scarcity of the data. Here, we develop a novel artificial intelligence framework integrating deep reinforcement learning (DRL) techniques with density functional theory simulations to automate the quantitative search and evaluation on the complex catalytic reaction networks from zero knowledge. Our framework quantitatively transforms the first-principles-derived free energy landscape of the chemical reactions to a DRL environment and the corresponding actions. By interacting with this dynamic environment, our model evolves by itself from scratch to a complete reaction path. We demonstrate this framework using the Haber-Bosch process on the most active Fe(111) surface. The new path found by our framework has a lower overall free energy barrier than the previous study based on domain knowledge, demonstrating its outstanding capability in discovering complicated reaction paths. Looking forward, we anticipate that this framework will open the door to exploring the fundamental reaction mechanisms of many catalytic reactions

Additional file 1 of Global, regional, and national burden of hypertensive heart disease during 1990–2019: an analysis of the global burden of disease study 2019

Additional file 1: Figure S1. The age-standardized prevalence (A), death (B), and DALY (C) cases for hypertensive heart disease by different continents, 1990-2019

Discovering Catalytic Reaction Networks Using Deep Reinforcement Learning from First-Principles

Determining the reaction pathways, which is central to illustrating the working mechanisms of a catalyst, is severely hindered by the high complexity of the reaction and the extreme scarcity of the data. Here, we develop a novel artificial intelligence framework integrating deep reinforcement learning (DRL) techniques with density functional theory simulations to automate the quantitative search and evaluation on the complex catalytic reaction networks from zero knowledge. Our framework quantitatively transforms the first-principles-derived free energy landscape of the chemical reactions to a DRL environment and the corresponding actions. By interacting with this dynamic environment, our model evolves by itself from scratch to a complete reaction path. We demonstrate this framework using the Haber-Bosch process on the most active Fe(111) surface. The new path found by our framework has a lower overall free energy barrier than the previous study based on domain knowledge, demonstrating its outstanding capability in discovering complicated reaction paths. Looking forward, we anticipate that this framework will open the door to exploring the fundamental reaction mechanisms of many catalytic reactions

Additional file 7 of Global, regional, and national burden of hypertensive heart disease during 1990–2019: an analysis of the global burden of disease study 2019

Additional file 7: Table S5. The age-standardized rate of hypertensive heart disease in different regions

Additional file 4 of Global, regional, and national burden of hypertensive heart disease during 1990–2019: an analysis of the global burden of disease study 2019

Additional file 4: Table S2. The age-standardized rate of hypertensive heart disease in different SDI regions

Additional file 6 of Global, regional, and national burden of hypertensive heart disease during 1990–2019: an analysis of the global burden of disease study 2019

Additional file 6: Table S4. The prevalence, death and DALYs of hypertensive heart disease in different regions