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| http://dx.doi.org/10.1016/j.jhepr.2024.101211 | DOI Listing |
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Background: Mental health remains among the top 10 leading causes of disease burden globally, and there is a significant treatment gap due to limited resources, stigma, limited accessibility, and low perceived need for treatment. Problem Management Plus, a World Health Organization-endorsed brief psychological intervention for mental health disorders, has been shown to be effective and cost-effective in various countries globally but faces implementation challenges, such as quality control in training, supervision, and delivery. While digital technologies to foster mental health care have the potential to close treatment gaps and address the issues of quality control, their development requires context-specific, interdisciplinary, and participatory approaches to enhance impact and acceptance.
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Centre of Research for Development, University of Kashmir, Srinagar, 190006, India.
The fate and degradation of organic matter in aquatic systems is a vital link in nutrient cycling and sedimentation processes influenced by exogenous and endogenous factors, such as inputs from upstream sources, sediment suspension, and the decomposition of aquatic organisms. The interplay of organic carbon, microbes, and environmental factors shapes the distribution and degradation of organic matter. Characterizing the source distribution of sedimentary organic matter in aquatic systems using novel proxies can unravel new insights into the mechanisms that control its dispersal, preservation and fate, which is essential to understanding the global carbon and nitrogen cycles.
View Article and Find Full Text PDFClassification of Imagined Speech Signals Using Functional Connectivity Graphs and Machine Learning Models.
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Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, 247667, India.
EEG involves recording electrical activity generated by the brain through electrodes placed on the scalp. Imagined speech classification has emerged as an essential area of research in brain-computer interfaces (BCIs). Despite significant advances, accurately classifying imagined speech signals remains challenging due to their complex and non-stationary nature.
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