Tracing the early development of harmful algal blooms with the aid of Lagrangian coherent structures

Several theories have been proposed to explain the development of harmful algal blooms (HABs) produced by the toxic dinoflagellate \emph{Karenia brevis} on the West Florida Shelf. However, because the early stages of HAB development are usually not detected, these theories have been so far very difficult to verify. In this paper we employ simulated \emph{Lagrangian coherent structures} (LCSs) to trace the early location of a HAB in late 2004 before it was transported to an area where it could be detected by satellite imagery, and then we make use of a population dynamics model to infer the factors that may have led to its development. The LCSs, which are computed based on a surface flow description provided by an ocean circulation model, delineate past and future histories of boundaries of passively advected fluid domains. The population dynamics model determines nitrogen in two components, nutrients and phytoplankton, which are assumed to be passively advected by the simulated surface currents. Two nearshore nutrient sources are identified for the HAB whose evolution is found to be strongly tied to the simulated LCSs. While one nutrient source can be associated with a coastal upwelling event, the other is seen to be produced by river runoff, which provides support to a theory of HAB development that considers nutrient loading into coastal waters produced by human activities as a critical element. Our results show that the use of simulated LCSs and a population dynamics model can greatly enhance our understanding of the early stages of the development of HABs.Comment: Submitted to JGR-Ocean

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 153)

This bibliography lists 175 reports, articles, and other documents introduced into the NASA scientific and technical information system in March 1976

Innovative Earthquake Warning System for Enhancing Public Health Prevention

An earthquake is a sudden ground shaking caused by tectonic activity, posing significant risks to human safety. Effective public health strategies focus on minimizing damage, providing timely warnings, and ensuring response plans to reduce health impacts during seismic events. The purpose of this research is to establish an innovative earthquake warning system for enhancing public health prevention. In this study, we propose a novel Mexican Axolotl Optimization-tuned Adjustable Support Vector Regression (MAO-ASVR) for accurately detecting the earthquake incidents. Our model integrates Internet of Things (IoT) technology to collect data from various sensors deployed across seismically active regions. The obtained signal data is pre-processed using Noise Filtering technique. In our proposed model, MAO optimization algorithm iteratively fine-tunes the ASVR architecture for enhancing the detection accuracy. The system activates an early warning alert within seconds of detecting seismic activity and sends notification, thereby minimizing potential damage. During the findings analysis phase, we evaluate our model\u27s performance across various parameters. In addition, we also performed comparative analyses using diverse existing methodologies such as MAE with 0.397888, PMRE with 19.81432 errors, RMSE with 0.509074 errors. The findings demonstrate excellence and effectiveness of the suggested model

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 190, February 1979

This bibliography lists 235 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1979

Envisioning a marine biodiversity observation network

Author Posting. © University of California Press and American Institute of Biological Sciences, 2013. This article is posted here by permission of University of California Press and American Institute of Biological Sciences for personal use, not for redistribution. The definitive version was published in BioScience 63 (2013): 350-361, doi:10.1525/bio.2013.63.5.8.Humans depend on diverse ocean ecosystems for food, jobs, and sustained well-being, yet many stressors threaten marine life. Extensive research has demonstrated that maintaining biodiversity promotes ocean health and service provision; therefore, monitoring the status and trends of marine biodiversity is important for effective ecosystem management. However, there is no systematic sustained program for evaluating ocean biodiversity. Coordinating existing monitoring and building a proactive marine biodiversity observation network will support efficient, economical resource management and conservation and should be a high priority. A synthesis of expert opinions suggests that, to be most effective, a marine biodiversity observation network should integrate biological levels, from genes to habitats; link biodiversity observations to abiotic environmental variables; site projects to incorporate environmental forcing and biogeography; and monitor adaptively to address emerging issues. We summarize examples illustrating how to leverage existing data and infrastructure to meet these goals

Seafloor characterization using airborne hyperspectral co-registration procedures independent from attitude and positioning sensors

The advance of remote-sensing technology and data-storage capabilities has progressed in the last decade to commercial multi-sensor data collection. There is a constant need to characterize, quantify and monitor the coastal areas for habitat research and coastal management. In this paper, we present work on seafloor characterization that uses hyperspectral imagery (HSI). The HSI data allows the operator to extend seafloor characterization from multibeam backscatter towards land and thus creates a seamless ocean-to-land characterization of the littoral zone