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Dr. Abubakar’s investigate seeks to boost knowledge of tumor biology and its impact on treatment reaction and medical outcomes by adopting an ecosystem approach that experiments both of those the tumor and TME as dynamically interconnected parts in the tumor-associated ecosystem. By leveraging AI, in conjunction with various laboratory staining ways, he is undertaking deep phenotypic profiling of tumor tissues to uncover novel characteristics that may be joined with epidemiological and medical information to tell prognosis and/or treatment method choice-earning for screening-detectable cancers.

Fault spot in meshed energy networks by making use of hybrid sparse recent and voltage measurements together with RSFCL

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Enhanced recent controlled doubly fed induction generator model with grid integration below sub and super synchronous problems

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Advancement of fault ride by way of capacity of DFIG-primarily based wind turbine devices using superconducting fault existing limiter

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With all the escalating uptake of population-dependent cancer screening programs around the world, the incidences of benign and precursor lesions on tissue biopsies executed for suspected breast, colorectal, prostate, or lung cancers are very likely to continue to increase, underscoring the need for purely natural historical past research into screening-detectable cancers. Appropriately, Dr. Abubakar’s integrative analysis application in computational pathoepidemiology is centered on advancing scientific understanding in the part of tissue ecosystem disruption during the etiology, organic history, tumor heterogeneity, and medical results of screening-detectable cancers.

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SC currents minimization in dispersed era embedded distribution networks with optimum application of FCLs

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