Search Results (216)

Hurricane Helene made landfall near Perry (Latitude 30.1 N) in the Big Bend area of Florida with a central pressure of 939 hPa. It moved northwards creating devastating damage and loss of life; however, the greatest damage and number of fatalities occurred well to the north around the City of Ashville (Latitude 35.6 N) where extreme rainfall fell and some of the strongest wind gusts were reported. This paper describes the change in the hurricane’s structure as it tracked northwards, how it gathered tropical moisture from the Atlantic and a turning wind profile between the 850 hPa and 500 hPa elevations, which led to such extreme rainfall. This turning wind profile is shown to be associated with extreme rainfall and loss of life from drowning and landslides around the globe. The area around Ashville suffered 157 fatalities, which is a considerable proportion of the 250 fatalities so far recorded in the whole United Stares from Helene. This is of extreme concern and should be investigated in detail as the public expect the greatest impact from hurricanes to be confined to coastal areas near the landfall site. It is another example of increased death tolls from tropical cyclones moving inland and generating heavy rainfall. As the global population increases and inland centres become more urbanised, run off from such rainfall events increases, which causes greater devastation. Full article

Australian scuba fatalities over 50 years were examined to determine temporal changes over two consecutive periods, 1972–1999 and 2000–2021. The Australasian Diving Safety Foundation database and National Coronial Information System were searched to identify scuba deaths from 1972 to 2021. Historical data, police and witness reports, and autopsies were recorded and comparisons made between the two periods. Of 430 total deaths, 236 occurred during 1972–1999 and 194 during 2000–2021, with average annual fatalities of 8.4 and 8.8, respectively. The proportion of males reduced (83% to 76%) and median ages rose (33 to 47 years) with a large rise in the percentage of casualties among people aged 45 years or older (24% to 57%). There were increases in certified divers (64% to 81%) and in the proportion of divers who were with a buddy at the time of their incident (17% to 27%), as well as a decrease in out-of-gas incidents (30% to 25%). A reduction in primary drowning (47% to 36%) was accompanied by more than a doubling of cardiac-related disabling conditions (12% to 26%). The substantial increase in casualties’ ages and of the proportions of casualties aged 45 or more and of females between the periods indicate the inclusion of a broader cohort of participants and ageing of longtime divers. The reduction in primary drowning was likely due to increased training and improvements in equipment, particularly BCDs and pressure gauges. The rise in cardiac-related deaths was due to an older and more obese cohort. Improved health education and surveillance and improved dive planning are essential to reduce such deaths. Full article

Subfossil trees in growth position and their associated organic sediments serve as valuable archives of past ecologies, shedding light on coastal forest responses to post-Glacial sea-level rise. This paper offers an overview of the significance of drowned forests as both ecological and cultural records, with particular emphasis on Australian Indigenous connections to these landscapes. Indigenous use of and cultural connections to coastal trees and forests in Australian contexts are outlined, along with an overview of the formation and preservation processes of submerged forests and the methodological approaches used to study them. Case studies from across Australia illustrate the diversity of these records and their relevance to both science and heritage. The paper highlights the need for a regional database of subfossil trees and peats and underscores the importance of integrating Indigenous and scientific knowledge systems to deepen our understanding of environmental and cultural change. Full article

The growth of maritime transportation has been accompanied by a gradual increase in accident rates, drawing greater attention to the critical issue of man-overboard incidents and drowning. Traditional maritime search-and-rescue (SAR) methods are often constrained by limited efficiency and high operational costs. Over the past few years, drones have demonstrated significant promise in improving the effectiveness of search-and-rescue operations. This is largely due to their exceptional ability to move freely and their capacity for wide-area monitoring. This study proposes an enhanced SR-DETR algorithm aimed at improving the detection of individuals who have fallen overboard. Specifically, the conventional multi-head self-attention (MHSA) mechanism is replaced with Efficient Additive Attention (EAA), which facilitates more efficient feature interaction while substantially reducing computational complexity. Moreover, we introduce a new feature aggregation module called the Cross-Stage Partial Parallel Atrous Feature Pyramid Network (CPAFPN). By refining spatial attention mechanisms, the module significantly boosts cross-scale target recognition capabilities in the model, especially offering advantages for detecting smaller objects. To improve localization precision, we develop a novel loss function for bounding box regression, named Focaler-GIoU, which performs particularly well when handling densely packed and small-scale objects. The proposed approach is validated through experiments and achieves an mAP of 86.5%, which surpasses the baseline RT-DETR model’s performance of 83.2%. These outcomes highlight the practicality and reliability of our method in detecting individuals overboard, contributing to more precise and resource-efficient solutions for real-time maritime rescue efforts. Full article

Irrigation canals are essential for agriculture in semi-arid regions like northern Chile, yet their ecological impacts on wildlife remain underexplored. Here, we assessed the effects of a 900 m transect of a dry, polyethylene-covered irrigation canal in the Coquimbo region on local terrestrial fauna. A total of 121 reptiles, marsupials, and rodents were found in the canal, with 82.64% mortality. Our evidence suggests that the animals drowned when the water flux stopped or perished because of the extreme heat irradiated by the polyethylene geomembrane once the canal became empty. We highlight that these canals act as lethal pitfall traps for terrestrial vertebrates, as seen in other countries. Given the ecological impact that these waterways impose on local fauna, the implementation of mitigation strategies such as physical barriers, escape aids, and connectivity structures to reduce mortality is urgent in the region. Full article

Sudden unexpected death in epilepsy (SUDEP) is sudden, unexpected, witnessed or unwitnessed, nontraumatic, non-drowning death that occurs in a person with epilepsy. SUDEP is the leading cause of epilepsy-related death in adults with epilepsy, with an incidence of about 1.2 per 1000 person-years in the general epilepsy population. Recent studies have shown similar prevalence in the pediatric population too. Although the precise mechanism remains unclear, well-documented cases of SUDEP suggest that a generalized tonic clonic seizure-induced, centrally mediated change in cardiorespiratory function leads to terminal apnea and cardiac arrest. Risk factors include generalized tonic clonic seizure frequency, duration of epilepsy, nocturnal seizure, and certain genetic syndromes. Orexin, adenosine, and serotonin neurotransmission have been explored as novel drug targets to mitigate SUDEP risk. Neurostimulation and resective epilepsy surgery have been reported to have beneficial effects on long-term SUDEP risk as well. Future studies may aim to clarify the role of sleep and other comorbidities in SUDEP pathophysiology. Full article

Electrostatic detection is a highly accurate way to monitor system performance failures at an early stage. However, due to the weak electrostatic signal, it can be easily interfered with under complex real-world conditions, leading to a reduction in its monitoring capability. During the electrostatic monitoring of rolling bearings, noise can easily drown out the effective signal, making it difficult to extract fault characteristics. In order to solve this problem, a sparse representation based on cluster-contraction stagewise orthogonal matching pursuit (CcStOMP) is proposed to extract the fault features in the electrostatic signals of rolling bearings. The method adds a clustering contraction mechanism to the stagewise orthogonal matching pursuit (StOMP) algorithm, performs secondary filtering based on atom similarity clustering on the selected atoms in the atom search process, updates the support set, and finally solves the weights and updates the residuals, so as to reconstruct the original electrostatic signals and extract the fault feature components of rolling bearings. The method maintains fast convergence while analysing the extraction effect by comparing the measured signals of rolling bearing outer ring and bearing roller faults with the traditional StOMP algorithm, and the results show that the CcStOMP algorithm has obvious advantages in accurately extracting the fault features in the electrostatic monitoring signals of rolling bearings. Full article

Background: This study developed and validated the Aquatic Competence Assessment for Children (ACA-C), an instrument designed to measure personal aquatic competence index in children aged 6 to 12 years. Aquatic competence is essential for water safety and the promotion of healthy habits, yet few validated tools integrate its multiple dimensions. The ACA-C is based on an ecological approach, considering the interaction between the child and various aquatic environments, both artificial (swimming pools) and natural (seas, rivers, and lakes). It is structured into three dimensions: aquatic literacy, drowning prevention, and environmental education, facilitating its application in diverse contexts. Methods: The ACA-C was validated using the Delphi method, with experts in aquatic education and safety. Kendall’s W coefficient of concordance was employed to measure the level of agreement among judges, ensuring rigorous criteria for item selection. Additionally, pilot studies were conducted with children in controlled aquatic environments to refine the instrument’s structure and content. Results: The results demonstrated high reliability and validity of the ACA-C for assessing children’s aquatic competence. This tool enables the identification of both actual and perceived competence levels, guiding pedagogical strategies for improvement. The inclusion of environmental education reinforces a comprehensive approach, fostering safety, responsible decision making, and enjoyment of aquatic environments. Conclusions: This study provides a validated instrument for use in educational and preventive contexts, contributing to water safety and well-being. Full article

Rip currents are fast-moving, narrow channels of water that flow seaward from the shoreline, typically forming within the surf zone and extending beyond the wave-breaking region. These currents pose significant hazards to swimmers, contributing to numerous drowning incidents, especially with the increasing popularity of ocean recreation. Despite their prevalence, rip currents remain difficult to detect visually, and no universally reliable method exists for their identification by beachgoers. To address this challenge, this study presents a novel approach for detecting rip currents in optical images using wavelet-based edge detection and image convolution techniques. Five identification criteria were established based on previous literature and expert observations. The proposed program incorporates image augmentation, averaging, and frame aggregation to enhance generalization and accuracy. Experimental analysis involving four iterations and four wavelet bases demonstrated that using two iterations with the Daubechies wavelet yielded the highest interpretation accuracy (88.3%). Performance evaluation using a confusion matrix further confirmed an accuracy rate of 83.0%. The results indicate that the proposed method identifies rip currents in images, offering a valuable tool for researchers studying rip current patterns. This approach lays the groundwork for future advancements in rip current detection and related research. Full article

Side-Scan Sonar (SSS) is widely used in underwater rescue operations and the detection of seabed targets, such as shipwrecks, drowning victims, and aircraft. However, the quality of sonar images is often degraded by noise sources like reverberation and speckle noise, which complicate the extraction of effective features. Additionally, challenges such as limited sample sizes and class imbalances are prevalent in side-scan sonar image data. These issues directly impact the accuracy of deep learning-based target classification models for SSS images. To address these challenges, we propose a side-scan sonar image classification model based on joint image deblurring–denoising and a pre-trained feature fusion attention network. Firstly, by employing transform domain filtering in conjunction with upsampling and downsampling techniques, the joint image deblurring–denoising approach effectively reduces image noise while preserving and enhancing edge and texture features. Secondly, a feature fusion attention network based on transfer learning is employed for image classification. Through the transfer learning approach, a feature extractor based on depthwise separable convolutions and densely connected networks is trained to effectively address the challenge of limited training samples. Subsequently, a dual-path feature fusion strategy is utilized to leverage the complementary strengths of different feature extraction networks. Furthermore, by incorporating channel attention and spatial attention mechanisms, key feature channels and regions are adaptively emphasized, thereby enhancing the accuracy and robustness of image classification. Finally, the Gradient-weighted Class Activation Mapping (Grad-CAM) technique is integrated into the proposed model to ensure interpretability and transparency. Experimental results show that our model achieves a classification accuracy of 96.80% on a side-scan sonar image dataset, confirming the effectiveness of this method for SSS image classification. Full article

Although low-head dams in the USA provide water supply, irrigation, and recreation opportunities, many are unknown by regulators. Unfortunately, hundreds of drownings occur each decade at these dams from an entrapment current that can form immediately downstream. To explore the ability of deep learning to scan large areas of terrain to identify the locations of low-head dams, ArcGIS Pro and embedded deep learning models for object-based image analysis were investigated. The State of Indiana low-head dam dataset was selected for model training and validation. Aerial imagery (leaf-off conditions) captured from 2016 to 2018 for the nearly 94,000 km² area had a minimum resolution of 304.8 mm. A new Python code was developed that automated the generation of training images and searching was limited to 100 m wide river corridors. Due to bank vegetation, all low-head dams were assigned a visibility score to aid in training and performance analysis. A total of 19 backbone models were considered with single shot detection and options for RetinaNet, Faster R-CNN, and batch normalization. Additional identification classes were incorporated to overcome identification of visually similar objects. After four training iterations, the final trained model was a ResNet RetinaNet backbone model featuring 101 layers with an 83% recall rate for dams with high visibility and a 17% recall rate for those with moderate visibility. Full article

Teeth are highly durable and useful in forensic identification and studying the impact of environmental factors could aid forensic investigations. Accurate post-mortem interval (PMI) estimation using dental evidence is critical in legal contexts, it requires further exploration. Aims: This study included a scoping review investigating macroscopic and microscopic changes in teeth in various simulated environments (Part 1) and an experimental study assessing changes in teeth and restorations exposed to distilled water, saline water, acidic soil, and alkaline soil (Part 2). Methods: The scoping review analysed publications from five databases using keywords such as ‘Teeth’, ‘Dental’, ‘Water’, ‘Soil’, ‘Acid’, and ‘Forensic.’ The experimental study involved 40 human teeth photographed before and after a 90-day exposure period to record shade variations and macroscopic changes. Results: Part 1: Twenty-six relevant articles from 10 countries (1987–2022) were reviewed, with most focusing on human teeth (77%), unrestored teeth (54%), macroscopic changes (46%), and high-temperature environments (53%). Part 2: Teeth in distilled water (G1) showed no shade variation. In saline water (G2), 60% of teeth decreased in shade. In acidic soil (G3), 40% showed an increased shade, while 50% showed a decreased shade. In alkaline soil (G4), 70% of teeth showed an increased shade. Restorations exhibited minimal changes across environments. Conclusions: Studies on the macroscopic changes because of high temperature on teeth and dental restorative material are popular. Teeth exposed to alkaline and acidic soil showed the most changes in the structure. Full article

Flexible pressure sensors have drawn growing attention in areas like human physiological signal monitoring and human–computer interaction. Nevertheless, it still remains a significant challenge to guarantee their long-term stability while attaining a wide detection range, a minute pressure testing limit, and high sensitivity. Inspired by the wrinkles on animal skins, this paper introduces a flexible pressure sensor with wrinkled microstructures. This sensor is composed of a composite of reduced graphene oxide (rGO), single-walled carbon nanotubes (SWCNTs), and polydimethylsiloxane (PDMS). After optimizing the proportion of the composite materials, the flexible pressure sensor was manufactured using highly efficient heat-shrinkable films. It has a sensitivity as high as 15.364 kPa⁻¹. Owing to the wrinkled microstructures, the sensor can achieve an ultra-wide pressure detection range, with the maximum reaching 1150 kPa, and is capable of detecting water wave vibrations at the minimum level. Moreover, the wrinkled microstructures were locked by PDMS. The sensor acquired waterproof performance and its mechanical stability was enhanced. Even after 18,000 cycles of repeated loading and unloading, its performance remained unchanged. By combining with an artificial neural network, high-precision recognition of different sounds and postures when grasping different objects was realized, with the accuracies reaching 98.3333% and 99.1111%, respectively. Through the integration of flexible WIFI, real-time wireless transmission of sensing data was made possible. In general, the studied sensor can facilitate the application of flexible pressure sensors in fields such as drowning monitoring, remote traditional Chinese medicine, and intelligent voice. Full article

Introduction: Adolescent/young adult (AYA) patients with metastatic soft tissue sarcoma (STS) typically face a dismal prognosis. However, a subset of patients with incurable disease lives beyond two years. Due to the rarity of diagnoses and inherent heterogeneity within this population, a paucity of data exists regarding the experiences of AYAs with an indolent course (and how to best capture these experiences). With increasing biological insight and clinical experience, including the use of targeted or immune therapies, it is anticipated that more such patients will experience prolonged survival. Our pilot study aimed to describe the clinical characteristics and illness experiences of AYAs with incurable yet indolent metastatic STS who were living two years after their diagnoses. Our exploratory aim was to generate a conceptual framework that could subsequently be tested in a multi-center study with a larger cohort of patients. Materials and Methods: Patients with metastatic incurable STS, aged 15–39 years at diagnosis, and at least two years from diagnosis, were eligible. Patients were recruited over a two-year period at a quaternary children’s hospital with a comprehensive AYA oncology program. Participants completed a demographic form and PROMIS short form questionnaires for seven domains and answered an open-ended question. Responses to open-ended questions were coded independently by two authors and utilized to generate themes. Clinical variables were collected from medical records. Results: Five patients completed questionnaires. Mean age was 29.4 years (18.5–39.8 years) at diagnosis and 34 years (23.2–45.7 years) at study. Three patients were female; two were male; four were White; and one was Black/African American. Diagnoses included ASPSCR1::TFE3 alveolar soft part sarcoma; WWTR1::CAMTA1 epithelioid hemangioendothelioma; INI-1 deficient epithelioid sarcoma; EWSR1::NR4A3 extra-skeletal myxoid chondrosarcoma; and low-grade ARHGAP23::FER spindle cell malignancy, a novel fusion-driven sarcoma. Mean time since diagnosis was 4.5 years (2.6–6 years), and mean treatment duration was 4.2 years (1.5–6 years). On average, patients received 4.8 lines (range 2–8 lines) of antineoplastic therapy. All patients received at least one targeted therapy or immune checkpoint inhibitor. Patients reported increased fatigue and anxiety and decreased physical function compared to the standardized US reference population. Themes emerging from qualitative responses included managing physical symptoms, navigating feelings of guilt and inadequacy, self-reflection generating gratitude, and changing illness experiences over time. Conclusions: AYA patients living with incurable metastatic soft tissue sarcoma for more than two years were treated with multiple lines of antineoplastic therapy longitudinally. PROMIS data identified fatigue, anxiety, and decreased physical function within this population. Exploratory thematic analysis of qualitative responses generated concepts that could be further tested in an expanded cohort of patients. Full article

Background/Objectives: Virtual autopsy (virtopsy) is a new domain of research for interdisciplinary teams of radiologists and forensic specialists. This scoping review aims to underline the current state-of-the-art research using combined imaging modalities. Methods: We searched the PubMed database using the term virtopsy for articles that are available in free full text, indexed in the Medline Database, and published in English. The query returned 49 articles on this subject that have been published since 2002. Results: The main imaging modalities used for postmortem imaging were computed tomography (PMCT), angiography (PMCTA), magnetic resonance imaging (PMMRI), and ultrasonography (PMUS). PMCT is highly effective for detecting complex osseous injuries, tracing bullet trajectories, or identifying characteristic findings in drowning cases. PMCTA is valuable for evaluating vascular lesions, particularly in natural death cases. PMMRI is superior in analyzing soft tissues, including brain and spinal structures, cerebrospinal fluid, microbleeds, and laryngohyoid lesions, and identifying cardiomyopathies in young individuals. PMUS serves as an alternative, and its portability also allows for use in forensic settings. One specific situation observed was the increased number of studies published about virtopsy during the COVID-19 pandemic. Another aspect is the increased focus on this alternative to conventional autopsy in the regions where maneuvering of the deceased is limited according to cultural and social customs. Conclusions: We underline the advantages and limitations of each imaging modality used for virtopsy. Further studies need to be developed in order to gather supplementary data regarding the use of these imaging modalities in the new era of artificial intelligence in medicine. Full article