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The authors looked at the ability to grow S. platensis on a larger scale with reduced cost given that it is currently quite expensive to grow, but poses as an important food source in the future.

Mealworms (Tenebrio molitor) are important food sources for reptiles, birds, and other organisms, as well as for humans. However, the slow growth and low survival rate of mealworms cause problems for mass production. Since alloferon, a synthetic peptide, showed long-term immunological effects on mealworms, we hypothesized that alloferon would function as a growth promoter to maximize mealworm production. We discovered that the overall weight of the alloferon-containing gelatin diet group was 39.5-90% heavier, and the development time of the experimental group was shortened up to 20.6-39.6% than the control group.

Here recognizing the growing amount of plastic waste in the oceans, the authors sought to develop and test laser imaging for the identification of waste in water. They found that while possible, limitations such as increasing depth and water turbidity result in increasing blurriness in laser images. While their image processing methods were somewhat insufficient they identified recent methods to use deep learning-based techniques as a potential avenue to viability for this method.

Using the European Space Agency’s Gaia dataset, the authors analyzed the relationship between white dwarfs’ magnitudes and proper motions. They hypothesized that older white dwarf stars may have different velocities than younger ones, possibly that stars slow down as they age. They found that the white dwarfs in the dataset were substantially redder and higher magnitude (traits traditionally associated with older stars) as compared to their non-fast counterparts.

These authors mathematically deduce a model that explains the interesting (and unintuitive) physical phenomenon that occurs when water falls.

Several studies have applied different machine learning (ML) techniques to the area of forecasting solar photovoltaic power production. Most of these studies use weather data as inputs to predict power production; however, there are numerous practical issues with the procurement of this data. This study proposes models that do not use weather data as inputs, but rather use past power production data as a more practical substitute to weather-based models. Our proposed models demonstrate a better, cheaper, and more reliable alternatives to current weather models.

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.

In this study, the authors test the longevity of a anti-mite compound, amitraz, in commercially-sold strips and the age-dependent efficacy of these strips in preventing honey bee colony collapse by ectoparasitic mite Varroa destructor.

Metal-organic frameworks (MOFs) are promising new nanomaterials for use in the fight against climate change that can efficiently capture and convert CO₂ to other useful carbon products. This research used computational models to determine the reaction conditions under which MOFs can more efficiently capture and convert CO₂. In a cost-efficient manner, this analysis tested the hypothesis that pressure and temperature affect the efficacy of carbon capture and conversion, and contribute to understanding the optimal conditions for MOF performance to improve the use of MOFs for controlling greenhouse CO₂ emissions.

Here seeking to identify a method to remove harmful microplastics from water, the authors investigated the viability of using electrolysis to degrade microplastics in tap water. Compared to control samples, they found electrolysis treatment to significantly the number of net microplastics, suggesting that this treatment could potentially implemented into homes or drinking water treatment facilities.