Search

Abstract A series of energy-econometrics techniques were employed for a 5-year time span between 2016 and 2020. The tests of Environmental Kuznets Curve (EKC) hypothesis were conducted essentially to examine the significance of economic growth (GDP), energy consumption (EC), with energy intensity (EI), and on-road passenger vehicles (PV) as related to economic development on the mitigation of carbon emissions (CO2-eq) in the transportation industry of South Africa. The findings from the prevailing research imply that, with respect to South Africa’s transportation industry, CO2-eq emissions increased in the course of early phases of economic growth while it tends to decline at certain levels of economic threshold. Though the nation maintains the edge of turning points in both the industrial and circular economy. The results further indicate a nexus between GDP and EC, which consequently affect the CO2-eq emissions. The findings proffer the needs to monitor the EC from the long-run impacts alongside the short run impacts of the forecast. The per capita GDP from the short-run impacts of t-stat—(4.928) to the long run effects of t-stat—(5.033) rises, indicating its improper influence in the industry. To limit the use of fossil-based fuels, as demonstrated in the negative signal of EI for long-run impacts of a p-value (0.2835), then to the short run effects which possess a significant p-value. It also highlights the directional correlation surfacing between EC, EI and South Africa’s on-road PV. In the computation context, the series was determined to be stationary at its first differences, as evident by the R2 combined with the R2 (Adjusted) values of 0.9837 and 0.9827, respectively, for both long-run and short-run assessments. The indication of the research among others further reveals that public transportation systems of road and rail options, which have the potentials to incorporate alternative energy sources, can be the required efforts to mitigate climate change and global warming effects in the transportation industry.

Fault diagnosis plays a pivotal role in ensuring the reliability and efficiency of industrial machinery. While various machine/deep learning algorithms have been employed extensively for diagnosing faults in bearings and gears, the scarcity of data and the limited availability of labels have become a major bottleneck in developing data-driven diagnosis approaches, restricting the accuracy of deep networks. To overcome the limitations of insufficient labeled data and domain shift problems, an intelligent, data-driven approach based on the Triplex Transfer Long Short-Term Memory (TTLSTM) network is presented, which leverages transfer learning and fine-tuning strategies. Our proposed methodology uses empirical mode decomposition (EMD) to extract pertinent features from raw vibrational signals and utilizes Pearson correlation coefficients (PCC) for feature selection. L2 regularization transfer learning is utilized to mitigate the overfitting problem and to improve the model’s adaptability in diverse working conditions, especially in scenarios with limited labeled data. Compared with traditional transfer learning approaches, such as TCA, BDA, and JDA, which demonstrate accuracies in the range of 40–50%, our proposed model excels in identifying machinery faults with minimal labeled data by achieving 99.09% accuracy. Moreover, it performs significantly better than classical methods like SVM, RF, and CNN-based networks found in the literature, demonstrating the improved performance of our approach in fault diagnosis under varying working conditions and proving its applicability in real-world applications.

The growing consumer demand for unique products has made customization and personalization essential in manufacturing. This shift to low-volume, high-mix (LVHM) production challenges the traditional paradigms and creates difficulties for small and medium-sized enterprises (SMEs). Industry 5.0 emphasizes the importance of human workers and social sustainability in adapting to these changes. This study introduces a digital twin design tailored for LVHM production, focusing on the collaboration between human expertise and advanced technologies. The digital twin-based production optimization system (DTPOS) uses an intelligent simulation-based optimization model (ISOM) to balance productivity and social sustainability by optimizing job allocation and scheduling. The digital twin model fosters a symbiotic relationship between human workers and the production process, promoting operational excellence and social sustainability through local innovation and economic growth. A case study was conducted within the context of a printed circuit board assembly (PCBA) using surface mount technology to validate the digital twin model’s efficacy and performance. The proposed DTPOS significantly improved the performance metrics of small orders, reducing the average order processing time from 19 days to 9.59 days—an improvement of 52.63%. The average order-to-delivery time for small orders was 19.47 days, indicating timely completion. These findings highlight the successful transformation from mass production to mass personalization, enabling efficient production capacity utilization and improved job allocation and scheduling. By embracing the principles of Industry 5.0, the proposed digital twin model addresses the challenges of LVHM production, fostering a sustainable balance between productivity, human expertise, and social responsibility.

Designing environmentally friendly products has become a tighter requirement in the marketplace because of both the increasing trend in awareness of consumers and the obligations from legislation requirements. Unfortunately, this is not a straight forward decision for designers to evaluate whether their design options are satisfactory in terms of balancing various factors (for examples, different forms of environmental assessment) or not. This is partly attributed to the fact that there is no universally accepted approach for conducting such analysis. In this connection, this research makes reference to a European Union (EU) directive as a reference model and makes use of Analytic Hierarchical Process (AHP), which is a useful tool to help designers to make decision, for evaluating eco-design options. The AHP model is developed based on two case studies on consumer electronic products. Pairwise comparisons, one of the key steps in AHP, are conducted with the expertise gained from the case studies and the help from the software package Expert Choice. The paper also reveals how design options can be evaluated, or be screened out. The proposed method does not require the designers to conduct detailed analysis (life-cycle assessment for example) for every new product options and hence can save their time. This is particularly important when they are facing shorter and shorter product life cycle nowadays. First published online: 18 Jun 2014