Deep learning has substantially advanced the field of Single Image Super-Resolution (SISR). However, existing research has predominantly focused on raw performance gains, with little attention paid to quantifying the transferability of architectural components. In this paper, we introduce the concept of "Universality" and its associated definitions, which extend the traditional notion of "Generalization" to encompass the ease of transferability of modules. We then propose the Universality Assessment Equation (UAE), a metric that quantifies how readily a given module can be transplanted across models and reveals the combined influence of multiple existing metrics on transferability. Guided by the UAE results of standard residual blocks and other plug-and-play modules, we further design two optimized modules: the Cycle Residual Block (CRB) and the Depth-Wise Cycle Residual Block (DCRB). Through comprehensive experiments on natural-scene benchmarks, remote-sensing datasets, and other low-level tasks, we demonstrate that networks embedded with the proposed plug-and-play modules outperform several state-of-the-art methods, achieving a PSNR improvement of up to 0.83 dB or enabling a 71.3% reduction in parameters with negligible loss in reconstruction fidelity. Similar optimization approaches could be applied to a broader range of basic modules, offering a new paradigm for the design of plug-and-play modules.