The authors test whether basil can grow in a simulated Martian soil improved with a waste-based fertilizer called Milorganite.

Trihalomethanes, probable human carcinogens, are commonly found disinfection by-products (DBPs) in public water systems (PWS). The authors investigated the correlation between trihalomethane concentrations and socioeconomic factors in New York State, finding a negative correlation between median household income and trihalomethane concentrations. The inverse association between trihalomethanes and household income may indicate socioeconomic disparity regarding drinking water quality and the need for improved efforts to assist small- and medium-sized community water systems to lower DBP levels in New York State.

The authors propose a method to recycle Christmas tree needles into a non-toxic reducing agent for synthesizing copper nanoparticles.

Cell segmentation is the task of identifying cell nuclei instances in fluorescence microscopy images. The goal of this paper is to benchmark the performance of representative deep learning techniques for cell nuclei segmentation using standard datasets and common evaluation criteria. This research establishes an important baseline for cell nuclei segmentation, enabling researchers to continually refine and deploy neural models for real-world clinical applications.

Reinforcement learning (RL) is a form of machine learning that can be harnessed to develop artificial intelligence by exposing the intelligence to multiple generations of data. The study demonstrates how reply buffer reward mechanics can inform the creation of new pruning methods to improve RL efficiency.

Phagocytes feed by forming food vacuoles. In this article the authors investigate the extent that exposure of non-nutritional food, such as India Ink, to Tetrahymena pyriformis affects the number of vacuole formation. These studies provide insight to how organisms budget their energy and metabolic processes during an energy shortage.

Inherent bias is often the unconscious driver of human behavior, and the first step towards overcoming these biases is our awareness of them. In this article the authors investigate whether race, gender or species affect the choice of charity by middle class Spaniards. Their conclusions serve as a starting point for further studies that could help charities refine their campaigns in light of these biases effectively transcending them or taking advantage of them to improve their fundraising attempts.

Based on the success of deep learning, recent works have attempted to develop a waste classification model using deep neural networks. This work presents federated learning (FL) for a solution, as it allows participants to aid in training the model using their own data. Results showed that with less clients, having a higher participation ratio resulted in less accuracy degradation by the data heterogeneity.

Skin cancer is a common and potentially deadly form of cancer. This study’s purpose was to develop an automated approach for early detection for skin cancer. We hypothesized that convolutional neural network-based models using transfer learning could accurately differentiate between benign and malignant moles using natural images of human skin.

Here, recognizing the need to improve the efficiency of the conversion of solar energy to electrical energy, the authors used MATLAB to mathematically simulate a multi-layered thin film with an without an antireflective coating. They found that the use of alternating ZnO-SiO₂ multilayers enhanced the transmission of light into the solar cell, increasing its efficiency and reducing the reflectivity of the Si-Air interface.