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BackgroundWith the recent rapid industrialization, occupational safety and health (OSH) has become an important issue in all industrial and human activities. However, incidents of injuries and fatality rates in the Ghanaian industry sector continue to increase. Despite this increase, there is no evidence regarding the element of OSH management in transport activities in Ghanaian industries. Thus, this study aims to examine the perceptions regarding the importance of safety and health in work-related transport activities in Ghanaian industries.MethodsA survey data collection technique was used to gather information on best safety practices over a 5-month period. We randomly selected 298 respondents from industries to answer structured questionnaires. The respondents included drivers, transport managers, and safety engineers. Standard multiple regression model and Pearson product–movement correlation were used to performed the analysis.ResultsThe result shows that for interventions to improve safety and health, concentration has been on drivers’ safety practice with less attention to safe driving environments and vehicle usage. Additionally, the respondents are aware of the importance of OSH in transport activities, but the level of integration does not measure up to the standard to reduce operational accidents and injuries. Finally, strong commitment to changing unsafe practices at all levels of operations appears to be the effective way to improve safety situations.ConclusionOSH culture is not fully complied in industries transport activities. This study, therefore, supports the use of safety seminars and training sessions for industry workers responsible for transport operations for better integration of safety standards.

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We propose a new population-based optimization algorithm, named Social Network-based Swarm Optimization algorithm (SNSO), for solving unconstrained single-objective optimization problems. In SNSO, the population topology, neighborhood structure and individual learning behavior are used to improve the search performance of a swarm. Specifically, a social network model is introduced to adjust the population topology dynamically, so as to change the information flow among different individuals. Based on the new topology, an extended neighborhood strategy is provided to build a neighborhood for each individual. Different form other forms of neighborhoods, the new structure includes some real individuals connected to the current one and some virtual individuals having better fitness in history, which could bring to more useful information to individuals for avoiding invalid attempts. Furthermore, we propose a new learning framework that defines two different position update methods for two types of individuals with the aim of enhancing the diversity and search ability of the swarm. The performance of SNSO is compared with seven other swarm algorithms on twelve well-known benchmark functions. The experimental results show that SNSO has a better performance than the selected algorithms.

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