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Comparative study on three machine learning models in novel autonomous drone-based detection of invasive plant Brassica nigra

Ho et al. | Jul 05, 2026

Comparative study on three machine learning models in novel autonomous drone-based detection of invasive plant <em>Brassica nigra</em>

Autonomous drone imaging combined with machine learning offers a promising approach for early detection of invasive species. In this study, students built an autonomous drone and compared three models: CNN, SGDC, and XGBoost, to identify Brassica nigra from aerial footage. Their results show that CNNs most effectively recognize key visual features, demonstrating strong potential for supporting conservation and invasive plant management.

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OTGP: An innovative biometric authentication system with on-the-go passwords using a novel typing signature

Shirodkar et al. | Jun 08, 2026

OTGP: An innovative biometric authentication system with on-the-go passwords using a novel typing signature
Image credit: Shirodkar and Balasubramanian

This manuscript describes a new method of on-the-go passwords using typing characteristics. The authors developed a keyboard and keystroke recording setup and tested it with 30 participants. The results indicated the five chosen parameters are distinct across participants yet consistent across time for each participant, making it a plausible candidate for a behavior-based password system.

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The effects of a high-sucrose diet on the survival of Drosophila melanogaster from a bacterial infection

Warwick et al. | May 22, 2026

The effects of a high-sucrose diet on the survival of <i>Drosophila melanogaster</i> from a bacterial infection

Excess sucrose consumption has been associated with several health problems, including inflammation and potential negative effects on immune function. However, the exact relationship between sucrose intake and immunity remains unclear, especially during bacterial infections. This study examined how sucrose intake affected the survival of fruit flies following oral infection with the bacterial pathogen Serratia marcescens.

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An in silico molecular analysis of the antifungal properties of Ageratum conyzoides

Sathish et al. | Apr 28, 2026

An <i>in silico</i> molecular analysis of the antifungal properties of <i>Ageratum conyzoides</i>
Image credit: Bánh Bao Chiên

This study explores the interaction between precocene II and trichocethecene 3-O-acetyltransferase using molecular docking simulations. Computational analysis identified several potential binding sites on the enzyme surface and predicted favorable ligand-protein interactions involving key residues. These findings provide insight into how precocene II may interact with this enzyme and demonstrate the use of computational approaches to explore potential antifungal mechanisms.

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Effects of plant-derived smoke water on germination and growth in stressed plants

Ge et al. | Apr 08, 2026

Effects of plant-derived smoke water on germination and growth in stressed plants

Here the authors investigated the potential of plant-derived smoke water to mitigate the negative impacts of abiotic stressors, such as heat, salinity, and water fluctuations, on the germination and growth of various agricultural crops. Their findings suggest that smoke water, specifically from buckwheat, pea, and radish, significantly improves plant resilience against drought and excessive water stress, offering a viable strategy to bolster food security in the face of climate change.

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Evaluating the performance of Q-learning-based AI in auctions

Liu et al. | Nov 09, 2025

Evaluating the performance of Q-learning-based AI in auctions
Image credit: Liu and Liu

Advertising platforms like Google Ads use AI to drive the algorithms used to maximize advertisers benefits. This study shows that AI does not adjust it strategy based on auction type and highlights the limitations of AI running without explicit guidance.

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