Harmful algal bloom (HAB) occurs frequently and causes serious damage. To study the early-warning and prediction technology of HAB is of significance for the early-warning and prediction, ecological control, and disaster prevention and mitigation of HAB. This paper reviewed the research progress in the early-warning and prediction technologies of HAB, including transport prediction, specific factors critical value prediction, data-driven model, and ecological math model, and evaluated the advantages and disadvantages of these four types of technologies. Some new ideas were brought forward about the prediction of cyanobacterial growth rate based on cell characteristics, and the early-warning of cyanobacterial bloom based on algal community characteristics.
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JAMIA Open
February 2025
Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN 55905, United States.
Objectives: In the general hospital wards, machine learning (ML)-based early warning systems (EWSs) can identify patients at risk of deterioration to facilitate rescue interventions. We assess subpopulation performance of a ML-based EWS on medical and surgical adult patients admitted to general hospital wards.
Materials And Methods: We assessed the scores of an EWS integrated into the electronic health record and calculated every 15 minutes to predict a composite adverse event (AE): all-cause mortality, transfer to intensive care, cardiac arrest, or rapid response team evaluation.
Sci Rep
January 2025
College of Electrical and Information Engineering, Hunan Institute of Traffic Engineering, Hunan, Hengyang, 421001, China.
This study aims to explore the application value of big data technology (BDT) in enterprise information security (EIS). Its goal is to develop a risk prediction model based on big data analysis to enhance the information security protection capability of enterprises. A big data analysis system that can monitor and intelligently identify potential security risks in real-time is constructed by designing complex network analysis algorithms and machine learning models.
View Article and Find Full Text PDFSci Rep
January 2025
British Geological Survey, London, UK.
This study demonstrates that machine learning from seismograms, obtained from commonly deployed seismometers, can identify the early stages of slope failure in the field. Landslide hazards negatively impact the economy and public through disruption, damage of infrastructure and even loss of life. Triggering factors leading to landslides are broadly understood, typically associated with rainfall, geological conditions and steep topography.
View Article and Find Full Text PDFBMC Emerg Med
January 2025
PreHospen- Centre for Prehospital Research, Faculty of Caring Science, Work Life and Social Welfare, University of Borås, Borås, SE- 501 90, Sweden.
Background: In Sweden with about 10 million inhabitants, there are about one million primary ambulance missions every year. Among them, around 10% are assessed by Emergency Medical Service (EMS) clinicians with the primary symptom of dyspnoea. The risk of death among these patients has been reported to be remarkably high, at 11,1% and 13,2%.
View Article and Find Full Text PDFZhonghua Jie He He Hu Xi Za Zhi
January 2025
College of Pulmonary & Critical Care Medicine, Chinese PLA General Hospital, Beijing100091, China.
This review outlines significant clinical research developments in the field of critical care respiratory medicine from October 2023 to September 2024. In the post-pandemic era, the new global definition of acute respiratory distress syndrome (ARDS) has improved practicality and early warning capabilities, although further refinement through respiratory mechanics and multi-omics approaches is required. Novel patterns of pulmonary microbiota distribution in ARDS patients have emerged, with microbiota-host immune interactions significantly influencing clinical outcomes.
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