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Dr. Abubakar’s exploration seeks to enhance understanding of tumor biology and its influence on cure response and scientific results by adopting an ecosystem method that studies both of those the tumor and TME as dynamically interconnected parts from the tumor-associated ecosystem. By leveraging AI, at the side of many laboratory staining methods, He's executing deep phenotypic profiling of tumor tissues to uncover novel features that could be linked with epidemiological and scientific information to tell prognosis and/or therapy conclusion-generating for screening-detectable cancers.
Fault place in meshed electric power networks through the use of hybrid sparse present and voltage measurements coupled with RSFCL
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Enhanced existing controlled doubly fed induction generator model with grid integration less than sub and Tremendous synchronous situations
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Advancement of fault experience through abubakar capability of DFIG-based mostly wind turbine programs working with superconducting fault current limiter
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Together with the growing uptake of inhabitants-based mostly most cancers screening packages around the globe, the incidences of benign and precursor lesions on tissue biopsies executed for suspected breast, colorectal, prostate, or lung cancers are very likely to carry on to rise, underscoring the necessity for organic record research into screening-detectable cancers. Appropriately, Dr. Abubakar’s integrative study method in computational pathoepidemiology is focused on advancing scientific knowledge in to the purpose of tissue ecosystem disruption inside the etiology, natural historical past, tumor heterogeneity, and scientific outcomes of screening-detectable cancers.
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SC currents minimization in dispersed generation embedded distribution networks with optimal software of FCLs
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