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The Environmental Protection Agency (EPA) reports a significant number of heavy metal-contaminated sites across the United States. To address this public health concern, rhizoremediation using microbes has emerged as a promising solution. Here, a combination of soil microbes were inoculated in the rhizosphere in soil contaminated with 500 parts per million (ppm) of lead. Results showed rhizoremediation is an effective bioremediation strategy and may increase crop productivity by converting nonarable lands into arable lands.

Superabsorbent beads are remarkable, used throughout our daily lives for various practical applications. These beads, as suggested by their name, possess a unique ability to absorb and retain large quantities of liquids. This characteristic of absorbency makes them essential throughout the medical field, agriculture, and other critical industries as well as in everyday products. To create these beads, the process of photopolymerization is fast growing in favor with distinct advantages of cost efficiency, speed, energy efficiency, and mindfulness towards the environment. In this article, researchers explore the pairing of cheap monomers with accessible equipment for creation of superabsorbent beads via the photopolymerization process. This research substantially demonstrates the successful application of photopolymerization in producing highly absorbent beads in a low-cost context, thereby expanding the accessibility of this process for creating superabsorbent beads in both research and practical applications.

Here, seeking to identify an optimal method to classify tree species through remote sensing, the authors used a few machine learning algorithms to classify forest tree species through multispectral satellite imagery. They found the Random Forest algorithm to most accurately classify tree species, with the potential to improve model training and inference based on the inclusion of other tree properties.

The authors propose a method to recycle Christmas tree needles into a non-toxic reducing agent for synthesizing copper nanoparticles.

The authors set to test a system that would help with the dehumidification and overall management of greehouses.

Here, recognizing the recognizing the growing threat of non-biodegradable plastic waste, the authors investigated the ability to use a modified enzyme identified in bacteria to decompose polyethylene terephthalate (PET). They used simulations to screen and identify an optimized enzyme based on machine learning models. Ultimately, they identified a potential mutant PETases capable of decomposing PET with improved thermal stability.

In this study, the authors developed and assessed the accuracy of a machine learning algorithm to identify skin cancers using images of biopsies.

The authors explore how similar exoplanets are to Earth and whether they could be inhabited by humans and other living organisms.

Intelligent vehicles utilize a combination of video-enabled object detection and radar data to traverse safely through surrounding environments. However, since the most momentary missteps in these systems can cause devastating collisions, the margin of error in the software for these systems is small. In this paper, we hypothesized that a novel object detection system that improves detection accuracy and speed of detection during adverse weather conditions would outperform industry alternatives in an average comparison.

In this study, a deep learning model is used to classify post-traumatic stress disorder patients through novel markers to assist in finding candidate biomarkers for the disorder.