Data

The AlimurgITA portal is a user-friendly and effective tool for researching Wild Edible Plants (WEPs). It provides valuable information on alimurgic plant species, aiding conservation and potential applications (agricultural, food, etc.). Users can interact with authors to report errors and contribute to the knowledge base regarding local uses. The authors will update the site every six months to include new data. Currently, the online database contains data on 1116 taxa used in 20 Italian regions: updated scientific name and link to the site Acta Plantarum, family, main synonyms, common name in Italian and regional dialect, chorotype, life form, a map showing the regions where it is known to be used, the part used, how it is used, and the bibliography. From the home page, you can search for taxa by scientific name, and there are pages dedicated to summaries of the entries: scientific name, family, chorotype, life form, method of use, and part used. Additionally, within the FuD WE PIC Project, the AlimurgITA entity list is being integrated with Italian vegetation data from the European Vegetation Archive to model WEPs richness, identify diversity hotspots, and explore the relationship between WEPs diversity and habitat types.

The development of Natural Language Processing applications tailored for diverse Arabic-speaking users requires specialized Arabic corpora, which are currently lacking in existing Arabic linguistic resources. Therefore, in this study, a multidialectal parallel Arabic corpus is built, focusing on the travel and tourism domain. By leveraging the text generation and dialectal transformation capabilities of Large Language Models, an initial set of approximately 100,000 parallel sentences was generated. Following a rigorous multi-stage deduplication process, 50,010 unique parallel sentences were obtained from Modern Standard Arabic (MSA) and five major Arabic dialects—Saudi, Egyptian, Iraqi, Levantine, and Moroccan. This study presents the detailed methodology of corpus generation and refinement, describes the characteristics of the generated corpus, and provides a comprehensive statistical analysis highlighting the corpus size, lexical diversity, and linguistic overlap between MSA and the five dialects. This corpus represents a valuable resource for researchers and developers in Arabic dialect processing and AI applications that require nuanced contextual understanding.

Logistics operations demand real-time visibility and rapid response, yet minute-level traffic speed forecasting remains challenging due to heterogeneous data sources and frequent distribution shifts. This paper proposes a Deep Operator Network (DeepONet)-based framework that treats traffic prediction as learning a mapping from historical states and boundary conditions to future speed states, enabling robust forecasting under changing scenarios. We project logistics demand onto a road network to generate diverse congestion scenarios and employ a branch–trunk architecture to decouple historical dynamics from exogenous contexts. Experiments on both a controlled simulation dataset and the real-world Metropolitan Los Angeles (METR-LA) benchmark demonstrate that the proposed method outperforms classical regression and deep learning baselines in cross-scenario generalization. Specifically, the operator learning approach effectively adapts to unseen boundary conditions without retraining, establishing a promising direction for resilient and adaptive logistics forecasting.