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Coral bleaching is a fatal process that reduces coral diversity, leads to habitat loss for marine organisms, and is a symptom of climate change. This process occurs when corals expel their symbiotic dinoflagellates, algae that photosynthesize within coral tissue providing corals with glucose. Restoration efforts have attempted to repair damaged reefs; however, there are over 360,000 square miles of coral reefs worldwide, making it challenging to target conservation efforts. Thus, predicting the likelihood of bleaching in a certain region would make it easier to allocate resources for conservation efforts. We developed a machine learning model to predict global locations at risk for coral bleaching. Data obtained from the Biological and Chemical Oceanography Data Management Office consisted of various coral bleaching events and the parameters under which the bleaching occurred. Sea surface temperature, sea surface temperature anomalies, longitude, latitude, and coral depth below the surface were the features found to be most correlated to coral bleaching. Thirty-nine machine learning models were tested to determine which one most accurately used the parameters of interest to predict the percentage of corals that would be bleached. A random forest regressor model with an R-squared value of 0.25 and a root mean squared error value of 7.91 was determined to be the best model for predicting coral bleaching. In the end, the random model had a 96% accuracy in predicting the percentage of corals that would be bleached. This prediction system can make it easier for researchers and conservationists to identify coral bleaching hotspots and properly allocate resources to prevent or mitigate bleaching events.

The study explored converting Gracilaria seaweed waste—known for releasing toxic hydrogen sulfide when decomposed—into biochar as a sustainable solution for waste management and soil improvement.

Investigation of the potential of waxworm saliva, the secretion of Galleria mellonella, for plastic degradation.

The authors looked into eco-friendly alternatives for insulating material. They ultimately found that a polyurethane derived from eggshells was an effective insulator and further research into it is warranted.

Here the authors explored the development of biodegradable bioplastic films derived from mango peels as a sustainable solution to plastic pollution and greenhouse gas emissions from fruit waste. They optimized the film's mechanical properties and water resistance through adjusting processing conditions and incorporating plasticizers and a hydrophobic coating, ultimately demonstrating its potential as a bacteriostatic and biodegradable alternative to conventional plastic food wrap.

Hydrogels are commonly used in medicine, pharmaceuticals, and agriculture. Hydrogels absorb water by swelling and re-release this water by diffusion. This study sought to synthesize a biodegradable, cellulose-based hydrogel that is more effective at absorbing and re-releasing water than those produced by current methods. We tested the compressive strength of both the dry and swollen gels and the tensile strength of the swollen gels to elucidate the gel structure.

Sodium dodecyl sulfate (SDS), a detergent component, can harm plant growth when it contaminates soil and waterways. Authors explored the potential of lactic acid bacteria (LAB) to mitigate SDS-induced stress on plants.

The authors looked at the ability of eggshells to slow ice melting. They found that eggshells were able to increase ice melting time when crushed showing that they were an effective thermal barrier.

This study explores how different economic sectors, geographic regions, and greenhouse gas types might affect future global mean surface temperature (GMST) anomalies differently from historical patterns. Using the Finite Amplitude Impulse Response (FaIR) model and four Shared Socioeconomic Pathways (SSPs) — SSP126, SSP245, SSP370, and SSP585 — the research reveals that future contributions to GMST anomalies.

Here, in an effort to identify alternatives to oil-based plastic, the authors sought to investigate the effects of plasticizers on the mechanical properties and chemical composition of gelatin bioplastic matrices. Through measurements of their tensile strength and elongation at break, along with FTIR spectroscopy, they identified 3% w/v polyethylene glycol film as having the best performance in their study..