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Dr. Abubakar’s research seeks to boost knowledge of tumor biology and its influence on treatment method response and scientific outcomes by adopting an ecosystem tactic that scientific studies the two the tumor and TME as dynamically interconnected components on the tumor-related ecosystem. By leveraging AI, at the side of numerous laboratory staining approaches, he is performing deep phenotypic profiling of tumor tissues to uncover novel characteristics that can be connected with epidemiological and scientific knowledge to tell prognosis and/or remedy conclusion-producing for screening-detectable cancers.
Improvement of reference recent calculation plan for grid-facet converter in the course of unbalanced faults
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This "Cited by" depend includes citations to the following content articles in Scholar. The ones marked * could be diverse with the short article during the profile.
Improved present-day managed doubly fed induction generator model with grid integration underneath sub and super synchronous conditions
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Enhancement of fault experience via ability of DFIG-based wind turbine techniques working with superconducting fault present limiter
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With the expanding uptake of populace-primarily based most cancers screening packages world wide, the incidences of benign and precursor lesions on tissue biopsies carried out for suspected breast, colorectal, prostate, or lung cancers are most likely to carry on to rise, underscoring the necessity for organic history experiments into screening-detectable cancers. Accordingly, Dr. Abubakar’s integrative investigate method in computational pathoepidemiology is focused on advancing scientific knowledge in to the job of tissue ecosystem disruption while in the etiology, natural heritage, tumor heterogeneity, and clinical results of screening-detectable cancers.
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SC currents minimization in dispersed generation embedded mabubakarmaken distribution networks with optimal software of FCLs
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