Driving fatigue is a serious issue for the transportation sector, decreasing the driver's performance and increasing accident risk. This study aims to investigate how fatigue mediates the relationship between the nature of work factors and driving performance. The approach included a review of the previous studies to select the dimensional items for the data collection instrument. A pilot test to identify potential modification to the questionnaire was conducted, then structural equation modelling (SEM) was performed on a stratified sample of 307 drivers, to test the suggested hypotheses. Based on the results, five hypotheses have indirect relationships, four of which have a significant effect. Besides, the results show that driving fatigue partially mediates the relationship between the work schedule and driving performance and fully mediates in the relationship between work activities and driving performance. The nature of work and human factors is the most common reason related to road accidents. Therefore, the emphasis on driving performance and fatigue factors would thereby lead to preventing fatal crashes and life loss.
Implementing a safety program is an essential step toward improving safety performance. This research aims to develop an overall project success (OPS) model for building projects through investigating the direct and indirect impact of safety critical success factors (CSFs) on OPS mediated by safety program elements. First, interviews were carried out with experts in the Iraqi construction industry, and then a questionnaire survey was utilized to obtain feedback from construction professionals. The results revealed that 20 elements are needed to confirm and improve effectiveness. These elements were categorized into four constructs: management commitment and employee involvement, worksite analysis, hazard and prevention control, and health and safety training. The analysis confirms that the relationship between safety CSFs and OPS are mediated by safety program elements. These findings offer a glimmer of hope for implementing safety programs in the Iraqi construction sector, and can also be used to enhance safety performance.
The construction industry generates a substantial volume of solid waste, often destinated for landfills, causing significant environmental pollution. Waste recycling is decisive in managing waste yet challenging due to labor-intensive sorting processes and the diverse forms of waste. Deep learning (DL) models have made remarkable strides in automating domestic waste recognition and sorting. However, the application of DL models to recognize the waste derived from construction, renovation, and demolition (CRD) activities remains limited due to the context-specific studies conducted in previous research. This paper aims to realistically capture the complexity of waste streams in the CRD context. The study encompasses collecting and annotating CRD waste images in real-world, uncontrolled environments. It then evaluates the performance of state-of-the-art DL models for automatically recognizing CRD waste in-the-wild. Several pre-trained networks are utilized to perform effectual feature extraction and transfer learning during DL model training. The results demonstrated that DL models, whether integrated with larger or lightweight backbone networks can recognize the composition of CRD waste streams in-the-wild which is useful for automated waste sorting. The outcome of the study emphasized the applicability of DL models in recognizing and sorting solid waste across various industrial domains, thereby contributing to resource recovery and encouraging environmental management efforts.
The purpose of this study is to review the relationship between the highly anticipated concept of circular economy (CE) and sustainable development goals (SDGs). These two sustainability principles have transformed organizations and countries in their quest to achieve sustainable development. Despite their importance to the business and corporate realm, the discussion of these two concepts has been developed in silos, arbitrarily connected. Through a bibliometric approach, this study reviewed 226 journal publications and 16,008 cited references from the Web of Science (WoS) to understand the past, present and future trends of the two concepts and their impact on the sustainability development. The bibliometric approach of citation, co-citation and co-word analysis uncovers the relevant and significant themes and research streams. Theoretical and practical implications were discussed within the broader business and governance perspective to develop a substantial triple bottom line in creating a sustainable future for civil society.
The increasing use of road traffic for land transportation has resulted in numerous road accidents and casualties, including those involving oil and gas tanker vehicles. Despite this, little empirical research has been conducted on the factors influencing tanker drivers' performance. This study aims to address this knowledge gap, particularly in the energy transportation industry, by examining the driving performance factors that affect tanker drivers and incorporating risk assessment measures. The model variables were identified from the literature and used to develop a survey questionnaire for the study. A total of 307 surveys were collected from Malaysian oil and gas tanker drivers, and the driving performance factors were contextually adjusted using the Exploratory Factor Analysis (EFA) approach. The driving performance model was developed using partial least squares structural equation modeling (PLS-SEM). The EFA results categorized driving performance into two constructs: 1) drivers' reaction time with β = 0.320 and 2) attention and vigilance with β value = 0.749. The proposed model provided full insight into how drivers' reaction time, attention, and vigilance impact drivers' performance in this sector, which can help identify potential risks and prevent accidents. The findings are significant in understanding the factors that affect oil and gas drivers' performance and can aid in enhancing oil and gas transportation management by including effective risk assessment measures to prevent fatal crashes.