The Alexandria3K library, a versatile Python-based tool, has been expanded to include the integra- tion of the PubMed dataset, enriching its capabil- ities in the analysis of scientific papers. Origi- nally supporting major datasets like Crossref and US patents, and smaller yet significant datasets like ORCID. The addition of PubMed enables in-depth analysis of medical papers, with medical specific data and articles not yet in the Crossref dataset. This research focused on validating the integration of PubMed into Alexandria3K. To achieve this, two literature surveys were replicated using the com- plete PubMed dataset. The first survey involved querying different pathogens in the dataset for three regions. The results were comparable, although some articles were missed by Alexandria3K but two articles were also missed by the original sur- vey. The second survey revolved around software tools used in medical papers. Although fewer ar- ticles were found with Alexandria3K the ratio for most tools was still comparable. Although a thor- ough manual review of all articles could have fur- ther refined the reevaluation, time constraints pre- vented this step. These replicated surveys demon- strate Alexandria3K’s potential in conducting lit- erature surveys, underscoring the need for manual validation to complement its capabilities.

Traffic prediction plays a big role in efficient transport planning capabilities and can reduce traffic congestion. In this study the application of Long Short-Term Memory (LSTM) models for predicting traffic volumes across varying prediction horizons is investigated. The data used is collected by the municipality of The Hague for a single month. The study focuses on comparing the performance of the LSTM across different time horizons up to 10 hours in the future. To evaluate the performance of the LSTM models, two common evaluation measures are employed: Root Mean Square Error (RMSE) and Symmetric Mean Absolute Percentage Error (SMAPE). The baseline for the predictions is set at a 15-minute future forecast. Comparing the 1-hour prediction against the 10-hour predictions relative to the baseline RMSE, the RMSE increased threefold. However, the SMAPE first increases, but surprisingly after 6 hours starts to decrease again.