One new cerebroside, macaocerebroside A (1), and 15 known compounds (2–16) were isolated from Solanum macaonense for the first time. In an antineutrophilic inflammatory assay, four caffeic acid derivatives (2–5) were active against superoxide anion generation with IC₅₀ values of 3.3–4.8 μM; especially, 3 inhibited elastase release with an IC₅₀ value of 4.0 μM. Additionally, kaempferol (8) also showed inhibition against elastase release with an IC₅₀ value of 4.0 μM. In a cytotoxic assay, rutin (14) had selective moderate cytotoxicity toward human lung (A549), bladder (NTUB1), and prostate (DU145) cancer cell lines with IC₅₀ values of 34.6, 41.3, and 31.8 μM, respectively.

Urban traffic congestion poses persistent challenges to mobility, public safety, and governance efficiency in metropolitan areas. This study proposes an intelligent traffic flow forecasting framework based on an extended Long Short-Term Memory (xLSTM) model, specifically designed for real-time congestion prediction and proactive police dispatch support. Utilizing a real-world dataset collected from over 300 vehicle detector (VD) sensors, the proposed model integrates vehicle volume, speed, and lane occupancy data at five-minute intervals. Methodologically, the xLSTM model incorporates matrix-based memory cells and exponential gating mechanisms to enhance spatio-temporal learning capabilities. Model performance is evaluated using multiple metrics, including congestion classification accuracy, F1-score, MAE, RMSE, and inference latency. The xLSTM model achieves a congestion prediction accuracy of 87.3%, an F1-score of 0.882, and an average inference latency of 41.2 milliseconds—outperforming baseline LSTM, GRU, and Transformer-based models in both accuracy and speed. These results validate the system’s suitability for real-time deployment in police control centers, where timely prediction of traffic congestion enables anticipatory patrol allocation and dynamic signal adjustment. By bridging AI-driven forecasting with public safety operations, this research contributes a validated and scalable approach to intelligent transportation governance, enhancing the responsiveness of urban mobility systems and advancing smart city initiatives.