Hypertension management is poor in primary care settings of developing countries, where 75% of hypertensives are living. Exploring better ways to improve hypertension management and to decrease stroke and CVD death is needed such as introducing treatment algorithm. Therefore, we selected intervention counties from Xinjiang, an underdeveloped region in China, and introduced antihypertensive treatment algorithm, comprising locally available and affordable agents, to primary health providers since 1998. Program effects were evaluated using the data collected in various ways including cross-sectional screenings to population ≥30 years between 1998 and 2015 by comparing treatment and control rates of hypertension, changes in blood pressure (BP) levels and distribution, and proportion of case/total and NCD death for CVD and stroke. Compared to 1998–2000, treatment rate was improved by 2.78 fold (11.2% vs. 32.1%, P  < 0.001), and the overall and treated control rate were improved by 53.5 fold (0.2% vs. 10.7%, P  < 0.001) and by 16.8 fold (2.0% vs. 33.5%, P  < 0.001), respectively, in 2015. Mean SBP and DBP showed a net reduction by 33.7 mmHg (181.3 vs. 147.6 mmHg) and 21.3 mmHg (106.3 vs. 85.0 mmHg), respectively, in 2015, compared to 1998–2000 ( P  < 0.001), and stage III hypertension was reduced by 75.2% (33.5 vs. 8.3%, P  < 0.001). Compared to 1997–1999, stroke/NCD death was reduced by 34.1% in 2015–2017 (31.7 vs. 20.9%, P  = 0.006) in the intervention counties whereas by 7.5% in control county. Introduction of treatment algorithm helps improve hypertension management and reduce stroke death in resource-constricted primary settings.

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There are many deviation sources in the assembly process of aircraft complex thin-walled structures. To get important factors that affect quality, it is crucial to diagnose the key deviation resources. The deviation transfer between deviation sources and assembly parts has the characteristics of small sample size, nonlinearity, and strong coupling, so it is difficult to diagnose the key deviation sources by constructing assembly dimension chains. Therefore, based on the deviation detection data, transfer entropy and complex network theory are introduced. Integrating the depth-first traversal algorithm with degree centrality theory, a key deviation diagnosis method for complex thin-walled structures is proposed based on weighted transfer entropy and complex networks. The application shows that key deviation sources that affect assembly quality can be accurately identified by the key deviation source diagnosis method based on complex networks and weighted transfer entropy.