This study explores how emergency shelters can adapt to a multi-hazard environment by geographic information system (GIS) and takes Guangzhou as a case for analysis. The physical suitability of the overall urban resources was first assessed by aiming to select the suitable resources and safe locations for emergency shelters in the context of multiple disasters. Afterward, by analyzing the scale and spatial distribution of affected areas and populations under different types of disaster scenarios, the demand for different kinds of shelters were predicted. Lastly, taking into account the coverage of the affected people, shelters were allocated according to different conditions in the districts. This work will hopefully provide a reference for the construction of emergency shelters and help form emergency operations in order to mitigate the impact of hazards. The issues identified in the study need to be further studied in medium or small-scale cities.

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Flight crew performance is of great significance in keeping flights safe and sound. When evaluating the crew performance, quantitative detailed behavior information may not be available. The present paper introduces the Bayesian Network to perform flight crew performance evaluation, which permits the utilization of multidisciplinary sources of objective and subjective information, despite sparse behavioral data. In this paper, the causal factors are selected based on the analysis of 484 aviation accidents caused by human factors. Then, a network termed Flight Crew Performance Model is constructed. The Delphi technique helps to gather subjective data as a supplement to objective data from accident reports. The conditional probabilities are elicited by the leaky noisy MAX model. Two ways of inference for the BN-probability prediction and probabilistic diagnosis are used and some interesting conclusions are drawn, which could provide data support to make interventions for human error management in aviation safety.

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Both simple and complex rectovaginal fistula can be reliably repaired using a bulbocavernosus graft.

Purpose -The purpose of this paper is to study the effects of relational mechanisms and market contracts on cross-enterprise knowledge trading in supply chain and to examine the role of market contracts. Relational mechanism is categorized into indirect and direct relational mechanism in this paper. Cross-enterprise knowledge trading is categorized into explicit and tacit knowledge trading. The indirect relational mechanism is mainly expressed by knowledge brokers, while the direct relational mechanism consists of shared goals and trust. Design/methodology/approach -Multiple regression analysis was performed on questionnaire data from 256 Chinese manufacturing enterprises in supply chain in order to assess the relationships between relational mechanisms, market contracts and cross-enterprise knowledge trading. Findings -The results show that knowledge brokers and market contracts have significant and positive effects on explicit knowledge trading, but the effects on tacit knowledge trading are not significant. Shared goals and trust have significant and positive effects not only on explicit knowledge trading but also on tacit knowledge trading, while trust has a stronger positive effect on tacit knowledge trading than explicit knowledge trading. Finally, the moderating effects of market contracts are proven in the relationships between relational mechanisms and knowledge trading, excluding the relationship between knowledge brokers and tacit knowledge trading. Originality/value -Previous studies about the cross-enterprise knowledge trading in supply chain focused on theoretical research which did not match with reality, especially in China, where the relational mechanism in trading activities is strong. Based on relational exchange theory and transaction cost theory, a conceptual model for the effects of relational mechanisms and market contracts on cross-enterprise knowledge trading in supply chain is proposed in this paper, and then empirically tested using the data collected from 256 Chinese manufacturing enterprises in supply chain with multiple regression models. The findings provide a theoretical basis for knowledge trading participants selecting an appropriate governance mechanism to promote knowledge trading, and these also guide the knowledge trading among members of supply chain in practice.

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<p style='text-indent:20px;'>This article constructs a two-stage supply chain consisting of a manufacturer (producing both fuel vehicles (FV) and new energy vehicles) and a retailer (selling both fuel vehicles and new energy vehicles) based on dual credit policy, considers three different power structure models, including the vertical Nash game model, the manufacturer Stackelberg game model, the retailer Stackelberg game model, and explores the operational strategy issues of new energy vehicle (NEV) enterprises under the dual credit policy. By comparing the optimal equilibrium solutions under different channel power structures, our findings indicate that (1) When the demand function is linear, the vertical Nash game model can achieve the highest system profit of the automotive supply chain, and in the manufacturer Stackelberg game model, the profit of the automaker is higher than that of the retailer, while in the retailer Stackelberg game model, the profit of the automaker is lower than that of the retailer. (2) The demand and pricing for FV and NEV in different models are determined by the range of FV and NEV production costs, credit trading prices, and the proportion of NEV credits. (3) Both the increase in the credit trading prices and the proportion of NEV credits will promote increased profits for the manufacturer and retailer. (4) The increase of the price sensitivity coefficient will reduce the demand as well as the profit of auto supply chain members.</p>