The insights on the tightness of the Ziv-Zakai bounds in the high-noise and low-noise regimes can be leveraged in various ways to design new estimation algorithms or improve existing ones. In the high-noise regime, where the bounds may not be tight, researchers can explore incorporating additional constraints or information into the estimation algorithms to improve their performance. For example, by combining the Ziv-Zakai bounds with regularization techniques or Bayesian priors, it may be possible to achieve tighter bounds and more accurate estimations in noisy environments. In the low-noise regime, where the bounds are tight, these insights can guide the development of efficient estimation algorithms that leverage the strengths of the Ziv-Zakai bounds to achieve optimal performance. By understanding the conditions under which the bounds are tight or sub-optimal, researchers can tailor their estimation algorithms to specific scenarios and optimize their performance accordingly.