Massive plastic accumulation has been taking place across diverse landscapes since the 1950s, when large-scale plastic production started. Nowadays, societies struggle with continuously increasing concerns about the subsequent pollution and environmental stresses that have accompanied this plastic revolution. Degradation of used plastics is highly time-consuming and causes volumetric aggregation, mainly due to their high strength and bulky structure. The size of these agglomerations in marine and freshwater basins increases daily. Exposure to weather conditions and environmental microflora (e.g., bacteria and microalgae) can slowly corrode the plastic structure. As has been well documented in recent years, plastic fragments are widespread in marine basins and partially in main global rivers. These are potential sources of negative effects on global food chains. Cyanobacteria (e.g., Synechocystis sp. PCC 6803, and Synechococcus elongatus PCC 7942), which are photosynthetic microorganisms and were previously identified as blue-green algae, are currently under close attention for their abilities to capture solar energy and the greenhouse gas carbon dioxide for the production of high-value products. In the last few decades, these microorganisms have been exploited for different purposes (e.g., biofuels, antioxidants, fertilizers, and ‘superfood’ production). Microalgae (e.g., Chlamydomonas reinhardtii, and Phaeodactylum tricornutum) are also suitable for environmental and biotechnological applications based on the exploitation of solar light. Can photosynthetic bacteria and unicellular eukaryotic algae play a role for further scientific research in the bioremediation of plastics of different sizes present in water surfaces? In recent years, several studies have been targeting the utilization of microorganisms for plastic bioremediation. Among the different phyla, the employment of wild-type or engineered cyanobacteria may represent an interesting, environmentally friendly, and sustainable option.
Kernelization is a general theoretical framework for preprocessing instances of NP-hard problems into (generally smaller) instances with bounded size, via the repeated application of data reduction rules. For the fundamental Max Cut problem, kernelization algorithms are theoretically highly efficient for various parameterizations. However, the efficacy of these reduction rules in practice-to aid solving highly challenging benchmark instances to optimality-remains entirely unexplored.We engineer a new suite of efficient data reduction rules that subsume most of the previously published rules, and demonstrate their significant impact on benchmark data sets, including synthetic instances, and data sets from the VLSI and image segmentation application domains. Our experiments reveal that current state-of-the-art solvers can be sped up by up to multiple orders of magnitude when combined with our data reduction rules. On social and biological networks in particular, kernelization enables us to solve four instances that were previously unsolved in a ten-hour time limit with state-of-the-art solvers; three of these instances are now solved in less than two seconds.
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