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Dr. Abubakar’s investigation seeks to enhance understanding of tumor biology and its influence on procedure response and clinical results by adopting an ecosystem technique that scientific studies each the tumor and TME as dynamically interconnected factors with the tumor-connected ecosystem. By leveraging AI, together with several laboratory staining techniques, he is accomplishing deep phenotypic profiling of tumor tissues to uncover novel capabilities which can be joined with epidemiological and medical data to tell prognosis and/or treatment final decision-producing for screening-detectable cancers.
Enhancement of reference existing calculation plan for grid-side converter through unbalanced faults
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Enhanced existing controlled doubly fed induction generator product with grid integration underneath sub and super synchronous circumstances
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Improvement of fault experience by means of capacity of DFIG-based mostly wind turbine systems employing superconducting fault current limiter
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Along with the rising uptake of population-based most cancers screening packages worldwide, the incidences of benign and precursor lesions on tissue biopsies performed for suspected breast, colorectal, prostate, or lung cancers are possible to continue to increase, underscoring the need for natural record reports into screening-detectable cancers. Accordingly, Dr. Abubakar’s integrative research method in computational pathoepidemiology is focused on advancing scientific understanding in the part of tissue ecosystem disruption within the etiology, natural heritage, tumor heterogeneity, and clinical outcomes of screening-detectable cancers.
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SC currents minimization in dispersed era embedded distribution networks with optimum software of FCLs
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