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PURPOSE: It is challenging to prepare military surgeons with the skills of combat damage control surgery (CDCS). The current study aimed to establish a damage control surgery (DCS) training platform for explosive combined thoraco-abdominal injuries. METHODS: The training platform established in this study consisted of 3 main components: (1) A 50 m × 50 m square yard was constructed as the explosion site. Safety was assessed through cameras. (2) Sixteen pigs were injured by an explosion of trinitrotoluene attached with steel balls and were randomly divided into the DCS group (accepted DCS) and the control group (have not accepted DCS). The mortality rate was observed. (3) The literature was reviewed to identify the key factors for assessing CDCS, and testing standards for CDCS were then established. Expert questionnaires were employed to evaluate the scientificity and feasibility of the testing standards. Then, a 5-day training course with incorporated tests was used to test the efficacy of the established platform. In total, 30 teams attended the first training course. The scores that the trainees received before and after the training were compared. SPSS 11.0 was employed to analyze the results. RESULTS: The high-speed video playback confirmed the safety of the explosion site as no explosion fragments projected beyond the wall. No pig died within 24 h when DCS was performed, while 7 pigs died in the control group. After a literature review, assessment criteria for CDCS were established that had a total score of 100 points and had 4 major parts: leadership and team cooperation, resuscitation, surgical procedure, and final outcome. Expert questionnaire results showed that the scientific score was 8.6 ± 1.25, and the feasibility score was 8.74 ± 1.19. When compared with the basic level, the trainees' score improved significantly after training. CONCLUSION: The platform established in this study was useful for CDCS training.

There is growing environmental psychology and behavior literature with mixed empirical evidence about the influence of public risk perceptions on the adoption of environmentally friendly "green behaviors". Adoption of stormwater green infrastructure on residential properties, while costlier in the short term compared to conventional greywater infrastructure, plays an important role in the reduction of nutrient loading from non-point sources into freshwater rivers and lakes. In this study, we use Bayesian Belief Networks (BBNs) to analyze a 2015 survey dataset (sample size = 472 respondents) about the adoption of green infrastructure (GSI) in Vermont's residential areas, most of which are located in either the Lake Champlain Basin or Connecticut River Basin. Eight categories of GSI were investigated: roof diversion, permeable pavement, infiltration trenches, green roofs, rain gardens, constructed wetlands, tree boxes, and others. Using both unsupervised and supervised machine learning algorithms, we used Bayesian Belief Networks to quantify the influence of public risk perceptions on GSI adoption while accounting for a range of demographic and spatial variables. We also compare the effectiveness of the Bayesian Belief Network approach and logistic regression in predicting the pro-environmental behaviors (adoption of GSI). The results show that influencing factors for current adoption differ by the type of GSI. Increased perception of risk from stormwater issues is associated with the adoption of rain gardens and infiltration trenches. Runoff issues are more likely to be considered the governments' (town, state, and federal agencies) responsibility, whereas lawn erosion is more likely to be considered the residents' responsibility. When using the same set of variables to predict pro-environmental behaviors (adoption of GSI), the BBN approach produces more accurate predictions compared to logistic regression. The results provide insights for further research on how to encourage residents to take measures for mitigating stormwater issues and stormwater management.