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AeroPurify: Autonomous air filtration UAV using real-time 3-D Monte Carlo gradient search

Kadakia et al. | Sep 01, 2025

AeroPurify: Autonomous air filtration UAV using real-time 3-D Monte Carlo gradient search
Image credit: Ian Usher

Here the authors present an autonomous drone air filtration system that uses a novel algorithm, the gradient ascent ML particle filter (GA/MLPF), to efficiently locate and mitigate outdoor air pollution. They demonstrate that their GA/MLPF algorithm is significantly more efficient than the conventional gradient ascent algorithm, reducing both the time and number of waypoints needed to find the source of pollution.

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Testing filtration capabilities of household fabrics for protection against airborne contaminants

Shah et al. | May 31, 2025

Testing filtration capabilities of household fabrics for protection against airborne contaminants

Toxic particulates in the atmosphere pose significant health risks, and while modern masks can help reduce inhalation of these pollutants, their availability may be limited during health crises. This study evaluated the effectiveness of household fabrics (cotton, fleece, wool, and rayon) as particulate filters, finding that cotton outperformed the others in filtration efficiency, while rayon was the least effective. The findings suggest that cotton is a preferable alternative for filtration purposes, while rayon should be avoided.

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Development of novel biodegradable bioplastics for packaging film using mango peels

Wang et al. | Apr 06, 2025

Development of novel biodegradable bioplastics for packaging film using mango peels
Image credit: JACQUELINE BRANDWAYN

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.

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Effects of urban traffic noise on the early growth and transcription of Arabidopsis thaliana

Kim et al. | Sep 18, 2024

Effects of urban traffic noise on the early growth and transcription of <i>Arabidopsis thaliana<i>

This article explores the largely unstudied impact of noise pollution on plant life. By exposing Arabidopsis thaliana seedlings to urban traffic noise, the study found a significant increase in seedling growth, alongside substantial changes in gene expression. This research reveals critical insights into how noise pollution affects plant physiology and contributes to a broader understanding of its ecological impacts, helping to guide future efforts in ecosystem conservation.

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Testing Simarouba amara’s therapeutic effects against weedicide-induced tumor-like morphology in planarians

Thiagarajan et al. | Apr 26, 2024

Testing Simarouba amara’s therapeutic effects against weedicide-induced tumor-like morphology in planarians

According to the World Health Organization, cancer is a leading cause of death globally. The disease’s prevalence is rapidly increasing in association with factors including the increased use of pesticides and herbicides, such as glyphosate, which is one of the most widely used herbicide ingredients. Natural antioxidants and phytochemicals are being tested as anti-cancer agents due to their antiproliferative, antioxidative, and pro-apoptotic properties. Thus, we aimed to investigate the potential role of S. amara extract as a therapeutic agent against glyphosate-induced toxicity and tumor-like morphologies in regenerating and homeostatic planaria (Dugesia dorotocephala).

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Groundwater prediction using artificial intelligence: Case study for Texas aquifers

Sharma et al. | Apr 19, 2024

Groundwater prediction using artificial intelligence: Case study for Texas aquifers

Here, in an effort to develop a model to predict future groundwater levels, the authors tested a tree-based automated artificial intelligence (AI) model against other methods. Through their analysis they found that groundwater levels in Texas aquifers are down significantly, and found that tree-based AI models most accurately predicted future levels.

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