KING'S COLLEGE LONDON
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This company is tracked across risk categories, including those related to its sector (e.g., Colleges, Universities, and Professional Schools, Commercial Economic, Sociological, and Educational Research), including supply chain integrity, ESG practices, labor disputes, and regulatory compliance.
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Recent Articles about KING'S COLLEGE LONDON
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2025-02-06 (pharmaceutical-journal.com)
José Moss (1965–2025) - The Pharmaceutical Journal
The Pharmaceutical Journal from the Royal Pharmaceutical Society
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2025-01-29 (responsesource.com)
58% of global ESG business regulations are voluntary
Only 42% of global ESG and sustainability policies are mandatory, finds the 2024 Carrots & Sticks report by researchers at King’s Business School and Th...
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2023-11-03 (yahoo.com)
Banks must guard against faster bank runs, BoE's Hauser says
Financial regulators will need to make sure that banks retain adequate financial buffers as advances in technology increase the risk of bank runs, a senior Bank of England official said on Friday. Challenges facing central banks included `` how to ensure that banks' liquidity insurance remains
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2019-11-19 (nature.com)
Tensor decomposition of TMS-induced EEG oscillations reveals data-driven profiles of antiepileptic drug effects
Transcranial magnetic stimulation combined with electroencephalography is a powerful tool to probe human cortical excitability. The EEG response to TMS stimulation is altered by drugs active in the brain, with characteristic “fingerprints” obtained for drugs of known mechanisms of action. However, the extraction of specific features related to drug effects is not always straightforward as the complex TMS-EEG induced response profile is multi-dimensional. Analytical approaches can rely on a-priori assumptions within each dimension or on the implementation of cluster-based permutations which do not require preselection of specific limits but may be problematic when several experimental conditions are tested. We here propose an alternative data-driven approach based on PARAFAC tensor decomposition, which provides a parsimonious description of the main profiles underlying the multidimensional data.
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