Evaluation of relationship between the rate of unsafe behaviors and personality trait Case study: construction project in a car manufacturing company

Introduction: According to the previous studies, about 90% of accidents in the workplace are due to the unsafe behaviors. In this study, the impact of personality traits, as a predictive factor on the unsafe acts was surveyed in a construction project of a car manufacturing company. .Material and Method: In this study, personality traits and unsafe behavior rates were determined using NEO-Five Factor Inventory (NEO-FFI) and safety behavior sampling (SBS) technique. The Spearman correlation coefficient was used. To analyze the acquired results, the total population of the project was 243 people. .Result: The results showed that 31.7% of workers’ behaviors were unsafe behaviors. The correlation between unsafe behaviors and the neuroticism and extroversion were direct and significant (p<0.001). . Conclusion: Assessment of the personality traits could be used as a predictive tool to identify employees with higher rates of unsafe behaviors and helps planning to reduce the accident rates

Keywords—XML, Tree Labeling, Query Processing.

Abstract—XML has become a popular standard for information exchange via web. Each XML document can be presented as a rooted, ordered, labeled tree. The Node label shows the exact position of a node in the original document. Region and Dewey encoding are two famous methods of labeling trees. In this paper, we propose a new insert friendly labeling method named IFDewey based on recently proposed scheme, called Extended Dewey. In Extended Dewey many labels must be modified when a new node is inserted into the XML tree. Our method eliminates this problem by reserving even numbers for future insertion. Numbers generated by Extended Dewey may be even or odd. IFDewey modifies Extended Dewey so that only odd numbers are generated and even numbers can then be used for a much easier insertion of nodes