Browse Articles

Impact of Soil Productivity on the Growth of Two Meyer Lemon Trees

Shen et al. | Dec 14, 2020

Impact of Soil Productivity on the Growth of Two Meyer Lemon Trees

Here, the authors aimed to apply home soil testing to identify the cause of the growth differences between two lemon trees. They hypothesized that differences in physical and chemical soil characteristics were influencing differences in soil productivity and plant growth. Overall, the study demonstrated the effectiveness of home soil testing to characterize soils and help homeowners solve common gardening problems.

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Developing a Method to Remove Inorganic Arsenic from Rice with Natural Substances

Mukai et al. | Oct 27, 2020

Developing a Method to Remove Inorganic Arsenic from Rice with Natural Substances

In this study, the authors tested different approaches for removing arsenic from rice. Due to higher arsenic levels in water, some areas grow rice with higher levels as well. This is a health hazard and so developing methods to remove arsenic from the rice will be helpful to many. Using a rapid arsenic kit, the authors found that activated charcoal was the most effective at removing arsenic from rice.

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Stereolithography 3D-printed stents with silica and zirconia nanoparticle reinforcement for intracranial aneurysm treatment

Panda et al. | Oct 12, 2026

Stereolithography 3D-printed stents with silica and zirconia nanoparticle reinforcement for intracranial aneurysm treatment
Image credit: Panda, Jeong, and Srinivasan

This manuscript focuses on improving the mechanical strength and radiopacity of SLA 3D-printed stent materials by incorporating nanoparticles for their application in flow-diverting stents (FDSs) used to treat aneurysms. The authors used biocompatible, bioresorbable resin formulations with varying concentrations of silica and zirconium to enhance tensile strength and radiopacity, respectively. The authors employed SEM to confirm the incorporation of nanoparticles, and CT imaging was used to assess the effect of zirconium-infused FDSs.

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Mitigating skin color bias in dermatology AI using CycleGAN-based data augmentation

Kannan et al. | Jun 24, 2026

Mitigating skin color bias in dermatology AI using CycleGAN-based data augmentation
Image credit: Kannan and Ramasamy

This study investigates skin tone bias in artificial intelligence models used for dermatological disease classification and evaluates a CycleGAN-based data augmentation approach to improve diagnostic performance on darker skin types. We generated synthetic dark-skinned images to enhance dataset diversity and compared model performance before and after augmentation. The results demonstrate that augmentation with synthetic dermatological images can help reduce disparities in diagnostic performance across skin tones, highlighting a practical strategy for improving fairness in dermatology AI systems.

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Researching the research enthusiasts: examining their motivation and the impact of a successful role model

Jubair et al. | Sep 18, 2024

Researching the research enthusiasts: examining their motivation and the impact of a successful role model
Image credit: The authors

High school and university students have various motivations for participating in research, ranging from strengthening their applications for university to building skills for a research career. Jubair and Islam survey Bangladeshi high school and university students to uncover their motivations and inspirations for participating in research.

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Using explainable artificial intelligence to identify patient-specific breast cancer subtypes

Suresh et al. | Jan 12, 2024

Using explainable artificial intelligence to identify patient-specific breast cancer subtypes

Breast cancer is the most common cancer in women, with approximately 300,000 diagnosed with breast cancer in 2023. It ranks second in cancer-related deaths for women, after lung cancer with nearly 50,000 deaths. Scientists have identified important genetic mutations in genes like BRCA1 and BRCA2 that lead to the development of breast cancer, but previous studies were limited as they focused on specific populations. To overcome limitations, diverse populations and powerful statistical methods like genome-wide association studies and whole-genome sequencing are needed. Explainable artificial intelligence (XAI) can be used in oncology and breast cancer research to overcome these limitations of specificity as it can analyze datasets of diagnosed patients by providing interpretable explanations for identified patterns and predictions. This project aims to achieve technological and medicinal goals by using advanced algorithms to identify breast cancer subtypes for faster diagnoses. Multiple methods were utilized to develop an efficient algorithm. We hypothesized that an XAI approach would be best as it can assign scores to genes, specifically with a 90% success rate. To test that, we ran multiple trials utilizing XAI methods through the identification of class-specific and patient-specific key genes. We found that the study demonstrated a pipeline that combines multiple XAI techniques to identify potential biomarker genes for breast cancer with a 95% success rate.

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Model selection and optimization for poverty prediction on household data from Cambodia

Wong et al. | Sep 29, 2023

Model selection and optimization for poverty prediction on household data from Cambodia
Image credit: Paul Szewczyk

Here the authors sought to use three machine learning models to predict poverty levels in Cambodia based on available household data. They found teat multilayer perceptron outperformed the other models, with an accuracy of 87 %. They suggest that data-driven approaches such as these could be used more effectively target and alleviate poverty.

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Efficient synthesis of superabsorbent beads using photopolymerization with a low-cost method

Wang et al. | Jun 09, 2023

Efficient synthesis of superabsorbent beads using photopolymerization with a low-cost method

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.

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