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Arctic waters have historically been relatively inaccessible for marine transport. Lately, climate change has made more of this region ice-free in the summer season. This has reduced the difficulty of marine transport in Arctic waters. Further, exploration and development of natural resources is increasing in Arctic regions, as is destinational shipping. The unique risk factors of this region, such as extremely low temperature, ice conditions and drifting icebergs, continue to pose threats to transportation. Potential impacts associated with marine transportation accidents warrant contingency plans that recognize that preventative measures may fail. To plan effectively, a transportation accident risk assessment model for Arctic waters is helpful. There is limited work on the development of such models. A new cause-consequences based risk assessment model is proposed here. The model estimates the probability of a transportation accident and also the related consequences during navigation in Arctic waters. To illustrate the application of the methodology, it is applied to a case of an oil-tanker collision on the Northern Sea Route.

Purpose Systemic inflammatory response index (SIRI) was an independent predictor of the prognosis of many diseases. Inflammatory prognostic index (IPI) was a new inflammatory prognostic marker with certain clinical significance. We aimed to explore the association between SIRI, IPI and early stage severity of stroke as well as 3-month outcome of AIS patients. Patients and Methods A total of 63 AIS patients who treated with alteplase were selected. The patients were divided into mild group and moderate to severe group according to the National Institutes of Health Stroke Scale (NIHSS) scores. According to the modified Rankin scale (mRS) score, patients were divided into the good prognosis group and the poor prognosis group. Spearman correlation statistically analyzed the correlation between SIRI, IPI and NIHSS score. Univariate and multivariate logistic regression analyzed the risk factors of 3-month prognosis. ROC curve was adopted to predict the effect of SIRI and IPI levels on poor prognosis in AIS patients. Results Spearman analysis showed that there was positively correlated with NIHSS score and IPI in mild AIS group ( r =0.541, P <0.05). Compared with the mild group, SIRI and IPI in the moderate to severe group was significantly higher ( P <0.05). The SIRI and IPI in the poor prognosis group were significantly higher than those in the good prognosis group ( P <0.05). Univariate and multivariate logistic regression analysis showed that SIRI and IPI were independent prognostic factors for the 3-month prognosis of AIS patients ( P < 0.05). The ROC curve showed that the areas under the 3-month prognosis curve predicted by SIRI and IPI were 0.685, 0.774 respectively. Conclusion IPI is correlated with stroke severity at admission. SIRI and IPI are independent predictors of short-term prognosis in AIS patients. SIRI and IPI can be a novel the good short-term prognostic biomarker for AIS patients treated with intravenous thrombolysis.

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