  By periodically returning a search process to a known or random state, random
resetting possesses the potential to unveil new trajectories, sidestep
potential obstacles, and consequently enhance the efficiency of locating
desired targets. In this chapter, we highlight the pivotal theoretical
contributions that have enriched our understanding of random resetting within
an abundance of stochastic processes, ranging from standard diffusion to its
fractional counterpart. We also touch upon the general criteria required for
resetting to improve the search process, particularly when distribution
describing the time needed to reach the target is broader compared to a normal
one. Building on this foundation, we delve into real-world applications where
resetting optimizes the efficiency of reaching the desired outcome, spanning
topics from home range search, ion transport to the intricate dynamics of
income. Conclusively, the results presented in this chapter offer a cohesive
perspective on the multifaceted influence of random resetting across diverse
fields.
