Search Results (2)

Background: The global climate crisis has intensified the demand for sustainable solutions, positioning Reverse Logistics (RL) as a critical strategy for minimizing environmental impacts. Simultaneously, Industry 4.0 technologies are transforming RL operations by enhancing their collection, transportation, storage, sorting, remanufacturing, recycling, and disposal processes. Understanding the roles of these technologies is essential for improving efficiency and sustainability. Methods: This study employs a systematic literature review, following the PRISMA methodology, to identify key Industry 4.0 technologies applicable to RL. Publications from Scopus and Web of Science were analyzed, leading to the development of a theoretical framework linking these technologies to RL activities. Results: The findings highlight the fact that technologies like the Internet of Things (IoT), Artificial Intelligence (AI), Big Data Analytics, Cloud Computing, and Blockchain enhance RL by improving traceability, automation, and sustainability. Their application optimizes execution time, reduces operational costs, and mitigates environmental impacts. Conclusions: For the transportation and manufacturing sectors, integrating Industry 4.0 technologies into RL can streamline supply chains, enhance decision-making, and improve resource utilization. Smart tracking, predictive maintenance, and automated sorting systems reduce waste and improve operational resilience, reinforcing the transition toward a circular economy. By adopting these innovations, stakeholders can achieve economic and environmental benefits while ensuring regulatory compliance and long-term competitiveness. Full article

Mesoscale Convective Systems (MCS) may vary greatly with respect to their morphology, propagation mechanism, intensity, and under which synoptic-scale conditions as a function of topographic complexity. In this study, we develop a long-term climatology of MCS during the North American Monsoon focusing on MCS morphology, lifecycle, and intensity as well as possible propagation mechanisms. We employ an MCS tracking and classification technique based on 23 years (1995 to 2017) of GOES IR satellite data. MCS intensity is also gauged with 7 years (2011 to 2017) of Vaisala GLD360 lightning data and, finally, monthly and interannual variability in synoptic conditions are examined with ERA5 reanalysis data. Our results based on 1594 identified MCS reveal that 98% are morphologically classified as Persistent Elongated Convective Systems. During the 23 summers (June through September) observed, the number of MCS varied considerably, averaging 70 MCS with minimum of 41 and maximum of 94. MCS typically have an average duration of around 8 h ± with a 2 h standard deviation. Propagation speeds, estimated with Hovmöller diagrams in addition to MCS centroid initial and final position, vary slightly depending on the trajectory. A notable result suggests that MCS propagation speeds are more consistent density currents or cold pools and not gravity waves nor steering-level winds. The results of this study could also provide a dataset for examining larger-scale controls on MCS frequency in addition to assesing convective parameterization and convective-resolving models in regions of complex topography. Full article