Enhancing water safety: Exploring recent technological approaches for drowning detection

Drowning poses a significant threat, resulting in unexpected injuries and fatalities. To promote water sports activities, it is crucial to develop surveillance systems that enhance safety around pools and waterways. This paper presents an overview of recent advancements in drowning detection, with a specific focus on image processing and sensor-based methods. Furthermore, the potential of artificial intelligence (AI), machine learning algorithms (MLAs), and robotics technology in this field is explored. The review examines the technological challenges, benefits, and drawbacks associated with these approaches. The findings reveal that image processing and sensor-based technologies are the most effective approaches for drowning detection systems. However, the image-processing approach requires substantial resources and sophisticated MLAs, making it costly and complex to implement. Conversely, sensor-based approaches offer practical, cost-effective, and widely applicable solutions for drowning detection. These approaches involve data transmission from the swimmer’s condition to the processing unit through sensing technology, utilising both wired and wireless communication channels. This paper explores the recent developments in drowning detection systems while considering costs, complexity, and practicality in selecting and implementing such systems. The assessment of various technological approaches contributes to ongoing efforts aimed at improving water safety and reducing the risks associated with drowning incidents

Genome-wide expression assay comparison across frozen and fixed postmortem brain tissue samples

<p>Abstract</p> <p>Background</p> <p>Gene expression assays have been shown to yield high quality genome-wide data from partially degraded RNA samples. However, these methods have not yet been applied to postmortem human brain tissue, despite their potential to overcome poor RNA quality and other technical limitations inherent in many assays. We compared cDNA-mediated annealing, selection, and ligation (DASL)- and <it>in vitro </it>transcription (IVT)-based genome-wide expression profiling assays on RNA samples from artificially degraded reference pools, frozen brain tissue, and formalin-fixed brain tissue.</p> <p>Results</p> <p>The DASL-based platform produced expression results of greater reliability than the IVT-based platform in artificially degraded reference brain RNA and RNA from frozen tissue-based samples. Although data associated with a small sample of formalin-fixed RNA samples were poor when obtained from both assays, the DASL-based platform exhibited greater reliability in a subset of probes and samples.</p> <p>Conclusions</p> <p>Our results suggest that the DASL-based gene expression-profiling platform may confer some advantages on mRNA assays of the brain over traditional IVT-based methods. We ultimately consider the implications of these results on investigations of neuropsychiatric disorders.</p

Star Cluster Candidates in M81

We present a catalog of extended objects in the vicinity of M81 based a set of 24 Hubble Space Telescope Advanced Camera for Surveys (ACS) Wide Field Camera (WFC) F814W (I-band) images. We have found 233 good globular cluster candidates; 92 candidate HII regions, OB associations, or diffuse open clusters; 489 probable background galaxies; and 1719 unclassified objects. We have color data from ground-based g- and r-band MMT Megacam images for 79 galaxies, 125 globular cluster candidates, 7 HII regions, and 184 unclassified objects. The color-color diagram of globular cluster candidates shows that most fall into the range 0.25 < g-r < 1.25 and 0.5 < r-I < 1.25, similar to the color range of Milky Way globular clusters. Unclassified objects are often blue, suggesting that many of them are likely to be HII regions and open clusters, although a few galaxies and globular clusters may be among them.Comment: 35 pages, 11 figures, submitted to A

Deep learning and 5G and beyond for child drowning prevention in swimming pools

Drowning is a major health issue worldwide. The World Health Organization’s global report on drowning states that the highest rates of drowning deaths occur among children aged 1–4 years, followed by children aged 5–9 years. Young children can drown silently in as little as 25 s, even in the shallow end or in a baby pool. The report also identifies that the main risk factor for children drowning is the lack of or inadequate supervision. Therefore, in this paper, we propose a novel 5G and beyond child drowning prevention system based on deep learning that detects and classifies distractions of inattentive parents or caregivers and alerts them to focus on active child supervision in swimming pools. In this proposal, we have generated our own dataset, which consists of images of parents/caregivers watching the children or being distracted. The proposed model can successfully perform a seven-class classification with very high accuracies (98%, 94%, and 90% for each model, respectively). ResNet-50, compared with the other models, performs better classifications for most classes.Peer ReviewedPostprint (published version

Salivary gland-specific <i>P. berghei</i> reporter lines enable rapid evaluation of tissue-specific sporozoite loads in mosquitoes

Malaria is a life-threatening human infectious disease transmitted by mosquitoes. Levels of the salivary gland sporozoites (sgs), the only mosquito stage infectious to a mammalian host, represent an important cumulative index of &lt;i&gt;Plasmodium&lt;/i&gt; development within a mosquito. However, current techniques of sgs quantification are laborious and imprecise. Here, transgenic &lt;i&gt;P. berghei&lt;/i&gt; reporter lines that produce the green fluorescent protein fused to luciferase (GFP-LUC) specifically in sgs were generated, verified and characterised. Fluorescence microscopy confirmed the sgs stage specificity of expression of the reporter gene. The luciferase activity of the reporter lines was then exploited to establish a simple and fast biochemical assay to evaluate sgs loads in whole mosquitoes. Using this assay we successfully identified differences in sgs loads in mosquitoes silenced for genes that display opposing effects on &lt;i&gt;P. berghei&lt;/i&gt; ookinete/oocyst development. It offers a new powerful tool to study infectivity of &lt;i&gt;P. berghei&lt;/i&gt; to the mosquito, including analysis of vector-parasite interactions and evaluation of transmission-blocking vaccines

Vision-based Propeller Damage Inspection Using Machine Learning

Unmanned Aerial Vehicles (UAVs) play an increasingly pivotal role in day-to-day rescue operations, offering crucial aerial support in challenging terrain and emergencies, such as drowning. Drone hangars are strategically deployed to ensure swift response in remote locations, overcoming range-limiting constraints posed by battery capacity. However, the UAV's airworthiness, typically ensured through conventional inspections by a technical individual, is paramount to guarantee mission safety. Over time, UAVs are prone to degradation through contact with the external environment, with propellers often being the cause of flight instability and potential crashes. This paper presents an innovative approach to automate UAV propeller inspection to avert incidents preemptively. Leveraging visual recordings and deep learning methodologies, we train a Convolutional Neural Network (CNN) model using both passive and active learning strategies. Our approach successfully detects physical damage on propellers with an impressive accuracy of 85.8%, promising a significant improvement in maintaining UAV flight safety and effectiveness in rescue operations

Human Detection for Flood Rescue: Application of YOLOv5 Algorithm and DeepSort Object Tracking

This thesis proposes a method of human detection using high-resolution surveillance cameras to monitor sections of the Chattahoochee River that require frequent search and rescue efforts due to flooding. The areas of interest are located in the city of Columbus, Georgia. The goals of this study are to evaluate the effectiveness of the YOLO (You Only Look Once) algorithm for human detection on the river as well as to propose future improvements to the city’s existing alert methods in the event of a flood.M.S