Browse Articles

In this article, the authors investigate whether stock selection across various sectors is efficient enough to outperform an overall market. Stocks from 2006 to 2020 were selected across sectors to calculate beta values using the Capital Asset Pricing Model.

In this research, a novel bioplastic inclusive of bamboo tannins and chitosan is selected from more than 60 trial formula variations based on resulting strength, fatigue, and transparency attributes. The biodegradability of the finalized bioplastic is compared to that of conventional polyethylene, in addition to investigating its solubility and water absorbance. This research displays the potential of a legitimate, fully biodegradable plastic alternative to current marketplace bioplastics.

In this work, the authors investigate the accuracy with which two different population growth models can predict population growth over time. They apply the Malthusian law or Logistic law to US population from 1951 until 2019. To assess how closely the growth model fits actual population data, a least-squared curve fit was applied and revealed that the Logistic law of population growth resulted in smaller sum of squared residuals. These findings are important for ensuring optimal population growth models are implemented to data as population forecasting affects a country's economic and social structure.

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.

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.

This manuscript evaluates peak detection algorithms for feature extraction in EMG-based hand gesture recognition using a random forest classifier. The study demonstrates that wavelet-based peak detection features achieve the highest classification accuracy (96.5%), outperforming other methods. The results highlight the potential of peak features to improve EMG-based prosthetic control systems.

The authors developed and tested machine learning methods to diagnose tuberculosis from pulmonary X-ray scans.

Black goji berries have attracted interest for their high levels of anthocyanin pigment, which believed to have health-boosting effects. Yu and Zhu research a method for measuring goji berry quality by detecting anthocyanin content under different conditions.

The authors looked at the ability of Spirulina platensis to reduce contaminants in wastewater in order to develop a more accessible treatment option. They found that S platensis did reduce the concentration of pollutants present within simulated agricultural wastewater.

Artificial sweeteners are rising in popularity, in part due to the influence of social media platforms like YouTube. However, YouTube commenters often repeat information about artificial sweeteners that is not supported by scientific research. To investigate how misinformation about sweeteners spreads through social media, Kim and Yoo conduct a content analysis of YouTube comments to reveal how many comments repeat misinformation about artificial sweeteners' effects.