Modeling leakage current of ceramic insulators subject to high pollution levels for improving maintenance activities

This paper presents a useful model to determine the washing periods and to indicate the pollution levels of electrical insulators. The behavior of leakage current, which is one indicator of the presence of pollutants on the insulator surfaces, was characterized through a regression model. We quantitatively examine the behavior of leakage current and the way electrical components are polluted. The data for environmental variables and leakage current in an electrical substation are analyzed and a model is identified that well represents the leakage current behavior on the insulators. Through this model, some predictions of the pollution effect can be made using analysis tools that enable the identification of the effects of leakage current on the entire network. This method can be used to obtain leakage current models on electrical substations located in highly polluted zones.Este documento presenta un modelo útil para determinar los períodos de lavado e indicar los niveles de contaminación de aisladores eléctricos. El comportamiento de la corriente de fuga, como indicador de la presencia de contaminantes en las superficies de los aisladores, se caracterizó a través de un modelo de regresión. Se examina cuantitativamente el comportamiento de la corriente de fuga y la contaminación de los componentes eléctricos. Se analizan los datos de las variables ambientales y la corriente de fuga en una subestación eléctrica y se identifica un modelo que representa bien el comportamiento de la corriente de fuga en los aisladores. Con este modelo, se pueden realizar predicciones del efecto de contaminación utilizando herramientas de análisis para identificar los efectos de la corriente de fuga en toda la red. Este método se puede usar para obtener modelos de corriente de fuga en subestaciones eléctricas ubicadas en zonas altamente contaminadas

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P < 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)