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Triggered largely by the warming and pollution of oceans, corals are experiencing bleaching and a variety of diseases caused by the spread of bacteria, fungi, and viruses. Identification of bleached/diseased corals enables implementation of measures to halt or retard disease. Benthic cover analysis, a standard metric used in large databases to assess live coral cover, as a standalone measure of reef health is insufficient for identification of coral bleaching/disease. Proposed herein is a solution that couples machine learning with crowd-sourced data – images from government archives, citizen science projects, and personal images collected by tourists – to build a model capable of identifying healthy, bleached, and/or diseased coral.

Muons, one of the fundamental elementary particles, originate from the collision of cosmic rays with atmospheric particles and are also generated in particle accelerator collisions. In this study, Samson et al analyze the factors that influence muon flux and lifetime using Cosmic Ray Muon Detectors (CRMDs). Overall, the study suggests that water can be used to decrease muon flux and that scintillator orientation is a potential determinant of the volume of data collected in muon decay studies.

Quorum sensing (QS) is the process in which bacteria recognize and respond to the surrounding cell density, and it can be inhibited by certain antimicrobial substances. This study showed that illumination intensity data is insufficient for evaluating QS activity without proper statistical modeling. It concluded that modeling illumination intensity through time provides a more accurate evaluation of QS activity than conventional cross-sectional analysis.

The authors looked at different algorithms and their ability to encrypt and decrypt text of various lengths.

The authors train a neural network to detect text-based emotions including joy, sadness, anger, fear, love, and surprise.

In this study the authors looked at the ability of the Discrete Fourier Transform (DFT) to analyze different musical elements. They found that DFT is a powerful method to analyze recorded music.

Here the authors conducted a photometric analysis of Supernova (SN) 20234jvj. Through generating a light curve, they determined SN 2023jvj to be a Type Ia supernova located approximately 1.246e8 parasecs away from Earth.

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 found that SARIMAX-GARCH is more accurate than SARIMAX for load forecasting with respect to energy consumption.

Physical activity when performed regularly has beneficial effects on all systems of the body, including pulmonary functions. This study, conducted at Springdales School in Dhaula Kuan, New Delhi, aimed to determine the effect of sports and singing on the vital capacity (the maximum amount of air a person can expel from the lungs after a maximum inhalation), an important measure of pulmonary health.