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Invasive species are increasing in number, extent and impact worldwide. Effective invasion management has thus become a core socio-ecological challenge. To tackle this challenge, integrating spatial-temporal dynamics of invasion processes with modelling approaches is a promising approach. The inclusion of dynamic processes in such modelling frameworks (i.e. dynamic or hybrid models, here defined as models that integrate both dynamic and static approaches) adds an explicit temporal dimension to the study and management of invasions, enabling the prediction of invasions and optimisation of multi-scale management and governance. However, the extent to which dynamic approaches have been used for that purpose is under-investigated. Based on a literature review, we examined the extent to which dynamic modelling has been used to address invasions worldwide. We then evaluated how the use of dynamic modelling has evolved through time in the scope of invasive species management. The results suggest that modelling, in particular dynamic modelling, has been increasingly applied to biological invasions, especially to support management decisions at local scales. Also, the combination of dynamic and static modelling approaches (hybrid models with a spatially explicit output) can be especially effective, not only to support management at early invasion stages (from prevention to early detection), but also to improve the monitoring of invasion processes and impact assessment. Further development and testing of such hybrid models may well be regarded as a priority for future research aiming to improve the management of invasions across scales.
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http://dx.doi.org/10.1016/j.jenvman.2017.03.060 | DOI Listing |
Implement Sci Commun
December 2024
Department of Pediatrics, Indiana University School of Medicine, 410 West 10th St., Indianapolis, IN, 46202, USA.
Background: Youth involved in the legal system have disproportionately higher rates of problematic substance use than non-involved youth. Identifying and connecting legal-involved youth to substance use intervention is critical and relies on the connection between legal and behavioral health agencies, which may be facilitated by learning health systems (LHS). We analyzed the impact of an LHS intervention on youth legal and behavioral health personnel ratings of their cross-system collaboration.
View Article and Find Full Text PDFBMC Health Serv Res
December 2024
School of Nursing, Health, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand.
Background: The COVID-19 pandemic exposed critical gaps in health system preparedness. This study, guided by a critical ecological model, examines the experiences of primary health and community services in Aotearoa New Zealand during the pandemic, focusing on their response to older people and their unpaid caregivers. The study aims to identify effective strategies for health system resilience.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Electrical Engineering, Gachsaran Branch, Islamic Azad University, Gachsaran, Iran.
Considering the widespread use of PHEVs in advanced societies and the issues ahead, researchers' thinking has focused more on this issue. The important issue is that the use of EVs is increasing due to the advantages, but the necessary infrastructure for their charging stations in the distribution networks does not exist. The high penetration level of EVs can create a potential risk for the existing distribution network; the fair charging of EVs has a special value.
View Article and Find Full Text PDFSci Rep
December 2024
Advanced Manufacturing Institute, King Saud University, Riyadh, 11421, Saudi Arabia.
Recently, social demands for a good quality of life have increased among the elderly and disabled people. So, biomedical engineers and robotic researchers aimed to fuse these techniques in a novel rehabilitation system. Moreover, these models utilized the biomedical signals acquired from the human body's particular organ, cells, or tissues.
View Article and Find Full Text PDFJ Neural Eng
December 2024
West China Hospital of Sichuan University, No.37 Guoxue Alley, Wuhou District, Chengdu City, Sichuan Province, Chengdu, Sichuan, 610041, CHINA.
Objective: Brain-computer interface(BCI) is leveraged by artificial intelligence in EEG signal decoding, which makes it possible to become a new means of human-machine interaction. However, the performance of current EEG decoding methods is still insufficient for clinical applications because of inadequate EEG information extraction and limited computational resources in hospitals. This paper introduces a hybrid network that employs a Transformer with modified locally linear embedding and sliding window convolution for EEG decoding.
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