A cluster-randomized controlled trial (RCT) was conducted to evaluate to the effectiveness of reducing mortality of upper gastrointestinal cancer (UGC) and feasibility of screening through a questionnaire combined with endoscopy in non-high-incidence urban areas in China. The trial design, recruitment performance, and preliminary results from baseline endoscopy are reported. Seventy-five communities in two urban cities with a nonhigh-incidence of UGC were randomized to a screening endoscopy arm (n ¼ 38) or a control arm (n ¼ 37). In the screening arm, individuals at high risk of UGC underwent endoscopic screening. The primary outcome was the UGC mortality, and secondary outcomes included the UGC detection rate, incidence rate, survival rate, and clinical stage at the time of diagnosis. A total of 10,416 and 9,565 individuals were recruited into the screening and control

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Power transmission line is one of the most important infrastructures of power system, and its safety monitoring is of great significance. The conventional way of fault monitoring of power transmission lines by only setting threshold value on single temperature data of strain clamp turned out to multiple misjudgements and delayed alarms, causing the increment of operation risk of power transmission line. In this paper, various types of time-varying sensors data such as strain clamp temperature data, environmental data, and cable ampacity data are accounted. Also, an unsupervised machine learning algorithm - K-means clustering algorithm was introduced to build a discriminant model in detecting the defects of power transmission line. Experimental results proved that the proposed method is able to avoid delayed alarms as well as misjudgement incurred from conventional method. As a result, the operation safety of power transmission line and inspection efficiency can be improved. The inspection cost would be reduced as well.

<p>Table S1 Comparison of screening and control arm on potential risk factors for upper gastrointestinal cancerï¼›Table S2 Multivariate logistic analysis of participation rates for endoscopy</p>