While serving as an immediate address for psychological safety and stability, psychological first aid (PFA) currently lacks the incorporation of triage. Without triage, patients cannot be prioritized in correspondence to condition severity that is often called for within emergency conditions. To disentangle the relevance of a potential triage system to PFA, the authors of this paper have developed a method to quantify resilience - a prominent predictor of the capability to recover from a disaster. With this resilience index, they have quantified resilience of differing age, race, and sex demographics to better inform the practice of PFA and potential demographic prioritization via a triage system.

In this study, the authors seek to improve a machine learning algorithm used for image classification: identifying male and female images. In addition to fine-tuning the classification model, they investigate how accuracy is affected by their changes (an important task when developing and updating algorithms). To determine accuracy, a set of images is used to train the model and then a separate set of images is used for validation. They found that the validation accuracy was close to the training accuracy. This study contributes to the expanding areas of machine learning and its applications to image identification.

In this work, the authors develop an algorithm that solves the problem of efficient space travel between planets. This is a problem that could soon be of relevance as mankind continues to expand its exploration of outer space, and potentially attempt to inhabit it.

In this study, the authors test different infill patterns to determine which would be the strongest and most durable for 3D printing applications, which have become an integral part of many facets of life.

This article discusses Alopecia areata, an autoimmune disorder causing sudden hair loss due to the immune system mistakenly attacking hair follicles. The article introduces the use of deep learning (DL) techniques, particularly convolutional neural networks (CNN), for classifying images of healthy and alopecia-affected hair. The study presents a comparative analysis of newly optimized CNN models with existing ones, trained on datasets containing images of healthy and alopecia-affected hair. The Inception-Resnet-v2 model emerged as the most effective for classifying Alopecia Areata.

The authors looked at how a student's own background influence their attitude towards integration of diverse cultures and ethnicities. While overall students viewed other groups positively, the authors found that groups still indicated they felt judged by their peers.

In this study, the authors look into some of the implications of rising carbon dioxide levels by studying the effects of acidic pH on the ability of T. pyriformis to feed by quantifying phagosome formation and motility.

High-fructose diets consumed widely in modern societies predisposes to metabolic diseases such as diabetes. Using the worm C. elegans, the authors of this study investigated the effect of fructose on the worm's survival rates. They found that worms fed 15% fructose had a lower life expectancy than those on a fructose-free diet. These results suggest that, like in humans, fructose has a negative effect on worm survival, which makes them an easy, attractive model to study the effects of fructose on health.

The density of stomata, or stomatal index, in plant leaves is correlated with the plant's rate of photosynthesis, and affected by the plant's climate. In this paper, authors measure the stomatal index of five plant species to derive their rates of photosynthesis. These results could help track changes in plants' photosynthetic rates with changing climate.

Arabadopsis, “the fruit fly of plants”, is an easy to grow plant system for genetic manipulation. Here, researchers tested the effects of varied light conditions on plants with specific mutations in the light sensing pathways.