With the rapid development of the cellular network, network planning is increasingly important. Generating large-scale urban cellular traffic contributes to network planning via simulating the behaviors of the planned network. Existing methods fail in simulating the long-term temporal behaviors of cellular traffic while cannot model the influences of the urban environment on the cellular networks. We propose a knowledge-enhanced GAN with multi-periodic patterns to generate large-scale cellular traffic based on the urban environment. First, we design a GAN model to simulate the multi-periodic patterns and long-term aperiodic temporal dynamics of cellular traffic via learning the daily patterns, weekly patterns, and residual traffic between long-term traffic and periodic patterns step by step. Then, we leverage urban knowledge to enhance traffic generation via constructing a knowledge graph containing multiple factors affecting cellular traffic in the surrounding urban environment. Finally, we evaluate our model on a real cellular traffic dataset. Our proposed model outperforms three state-of-art generation models by over 32.77%, and the urban knowledge enhancement improves the performance of our model by 4.71%. Moreover, our model achieves good generalization and robustness in generating traffic for urban cellular networks without training data in the surrounding areas.

Context: Cilnidipine (CN) is a novel dihydropyridine calcium antagonist that is practically insoluble in aqueous media and exhibits a low oral bioavailability or limited clinical efficacy. Objective: This study investigated the effects of three commercial and chemically diverse polymers – PVP, PVP/VA and Soluplus – on crystallization tendency and in vitro dissolution profiles of CN in order to determine an optimum carrier for composing the preferred solid dispersion (SD) of CN. Methods: All these co-evaporated systems were characterized up to 3 months by thermoanalytical (DSC), crystallographic (POM, PXRD), microscopic (SEM) and spectroscopic (FTIR) techniques. Results: The results showed that the polymers could be sorted by their effects of inhibiting CN crystallization in the ascending order: Soluplus, PVP/VA, PVP. The sequence was in accordance with that of the strength of drug–polymer hydrogen bonds revealed by FTIR spectra. It could be ascribed to relative hydrogen-bonding acceptor strengths of N-vinylpyrrolidone moiety in the polymer molecules. On the other hand, all the SDs showed enhanced dissolution profiles compared to pure CN alone. On their effects of enhancing CN dissolution, the polymers could be sorted in the descending order: Soluplus, PVP, PVP/VA. Conclusions: It implied that the dissolution behavior of CN could bear a close relationship to both hydration capacity and hydrogen-bonding interaction tendency of moieties of the polymers. It might suggest an optimal formulation for CN comprising both PVP and Soluplus.