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Dr. Abubakar’s research seeks to improve understanding of tumor biology and its impact on therapy response and scientific results by adopting an ecosystem technique that scientific tests equally the tumor and TME as dynamically interconnected elements in the tumor-connected ecosystem. By leveraging AI, together with a number of laboratory staining strategies, He's carrying out deep phenotypic profiling of tumor tissues to uncover novel features that can be connected with epidemiological and clinical details to inform prognosis and/or therapy conclusion-generating for screening-detectable cancers.
Development of reference recent calculation scheme for grid-aspect converter all through unbalanced faults
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Improved existing controlled doubly fed induction generator product with grid integration under sub and Tremendous synchronous problems
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Enhancement of fault experience via ability of DFIG-centered wind turbine methods employing superconducting fault current limiter
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Together with the escalating uptake of population-centered cancer screening applications all over the world, the incidences of benign and precursor lesions on tissue biopsies executed for suspected breast, colorectal, prostate, or lung cancers are probable to continue to increase, underscoring the need for purely natural historical past scientific studies into screening-detectable cancers. Appropriately, Dr. Abubakar’s integrative investigation plan in computational pathoepidemiology is centered on advancing scientific comprehension into the purpose of tissue ecosystem disruption inside the etiology, purely natural historical past, tumor heterogeneity, and scientific outcomes of screening-detectable cancers.
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SC currents minimization in distributed generation embedded distribution networks with optimal software of FCLs
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