Saudi Cultural Missions Theses & Dissertations
Permanent URI for this communityhttps://drepo.sdl.edu.sa/handle/20.500.14154/10
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Item Restricted Understanding the Influence of Cultural Factors on Process Safety Behaviours in the Saudi Arabian Oil and Gas Industry(The University of Sheffield, 2024-09-02) Algarni, Abdulrahman; Hoseyni, SeyedThe study proposed seeks to investigate the main cultural factors that can impact process safety behaviours with focus on risk perception within the Saudi Arabian oil and gas industry. The project, through the collected data seeks to suggest recommendations which can improve safety culture and reduce its impacts on process safety behaviours. The study is based on a mixed-methods approach which combines quantitative questionnaires with qualitative case study analysis. Tow quantitative questionnaires where designed and directed to selected managers and workers operating in the Saudi oil and gas industry. These two questionnaires investigated many cultural dimensions which can impact risk perception among participants. The case study provided by Alshahrani et al (n.d.) investigated the impact of national culture on safety behaviour among Saudi and non-Saudi employees operating in the petrochemical industry as part of the oil and gas industry. The main findings of this research proved that some cultural factors in the Saudi oil and gas industry are determinant in shaping the effectiveness of risk perception the process safety behaviour in general terms. There are clear indications from the questionnaires basically and the case study that cultural factors such as traditional and religious values, masculinity as related to risk taking behaviours, fatalistic attitudes, low perception of risk tolerance and risky optimism are currently impacting the level of risk perception in the Saudi oil and gas sector having as such a direct impact on process safety behaviours10 0Item Restricted Predicting Pedestrian Crossing Intention(Saudi Digital Library, 2024) Alofi, Afnan; Trivedi, MohanAutonomous vehicles face significant challenges in understanding pedestrian behavior, particularly in urban environments. The system must recognize pedestrians’ intentions and anticipate their actions to achieve intelligent driving. This paper focuses on predicting pedestrian crossings, aiming to enable oncoming vehicles to react in a timely manner. We investigate the effectiveness of various input modalities for pedestrian crossing prediction, including human poses, bounding boxes and ego vehicle speed features. We propose a novel lightweight architec- ture based on LSTM and attention to accurately identifying crossing pedestrians. Our methods evaluated on two widely used public datasets for pedestrian behavior, PIE and JAAD datasets, and our algorithm achieved a state-of-the-art performance in both datasets34 0