The Mediterranean Basin’s diverse climates and ecosystems have shaped a rich botanical heritage through centuries of selective cultivation, resulting in a wide array of horticultural plants with valuable therapeutic properties. The use of horticultural food plants as herbal remedies has become an integral part of traditional medicine in this geographical context. The present review aims to highlight the use of horticultural food plants (HFPs) in the context of traditional herbal medicine in the countries of the Mediterranean Basin and explore their traditional uses and therapeutic properties. A comprehensive ethnobotanical literature search was conducted on the food plants used as herbal medicine in the Mediterranean region using existing online scientific databases. Based on the literature review, 64 taxa used as medicinal plants by traditional users in the Mediterranean Basin were documented. Overall, horticultural plants are used in Mediterranean countries to treat a total of 573 ailments. Italy has the highest number of use reports (998), followed by Morocco (281) and Spain (193). Apiaceae (11 taxa), Cucurbitaceae (9 taxa), and Brassicaceae (8 taxa) are the most frequently cited families. The genus Allium is the most abundant in species (5).

In the era of big data, an ever-growing volume of information is recorded, either continuously over time or sporadically, at distinct time intervals. Functional Data Analysis (FDA) stands at the cutting edge of this data revolution, offering a powerful framework for handling and extracting meaningful insights from such complex datasets. The currently proposed FDA methods can often encounter challenges, especially when dealing with curves of varying shapes. This can largely be attributed to the method’s strong dependence on data approximation as a key aspect of the analysis process. In this work, we propose a free knots spline estimation method for functional data with two penalty terms and demonstrate its performance by comparing the results of several clustering methods on simulated and real data.

Functional Ordinary Kriging is the most widely used method to predict a curve at a given spatial point. However, uncertainty remains an open issue. In this article a distribution-free prediction method based on two different modulation functions and two conformity scores is proposed. Through simulations and benchmark data analyses, we demonstrate the advantages of our approach when compared to standard methods.

In this work, we introduce a novel approach for functional data approximation based on generalized P-splines with non-uniform and adaptively placed knots. The key innovation of our proposal is the integration of a conditioning-aware strategy for selecting both the number and positions of the knots, as well as the regularization parameters. By reformulating the Tikhonov regularization problem, we propose a computationally efficient criterion that controls model complexity while ensuring numerical stability. The resulting approximation framework not only improves the fit across the entire functional domain but also maintains compactness and robustness. Extensive numerical experiments conducted on both synthetic and real-world datasets demonstrate that our approach significantly outperforms traditional free knot and smoothing spline methods in terms of approximation error and conditioning.