  This article presents the Sorting Composite Quantile Regression Neural
Network (SCQRNN), an advanced quantile regression model designed to prevent
quantile crossing and enhance computational efficiency. Integrating ad hoc
sorting in training, the SCQRNN ensures non-intersecting quantiles, boosting
model reliability and interpretability. We demonstrate that the SCQRNN not only
prevents quantile crossing and reduces computational complexity but also
achieves faster convergence than traditional models. This advancement meets the
requirements of high-performance computing for sustainable, accurate
computation. In organic computing, the SCQRNN enhances self-aware systems with
predictive uncertainties, enriching applications across finance, meteorology,
climate science, and engineering.
