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This paper proposes a data preparation process for managing real-world kinematic data and detecting fishing vessels. The solution is a binary classification that classifies ship trajectories into either fishing or non-fishing ships. The data used are characterized by the typical problems found in classic data mining applications using real-world data, such as noise and inconsistencies. The two classes are also clearly unbalanced in the data, a problem which is addressed using algorithms that resample the instances. For classification, a series of features are extracted from spatiotemporal data that represent the trajectories of the ships, available from sequences of Automatic Identification System (AIS) reports. These features are proposed for the modelling of ship behavior but, because they do not contain context-related information, the classification can be applied in other scenarios. Experimentation shows that the proposed data preparation process is useful for the presented classification problem. In addition, positive results are obtained using minimal information., Comment: Sensors 2020

This paper develops a smooth model identification and self-learning strategy for dynamic systems taking into account possible parameter variations and uncertainties. We have tried to solve the problem such that the model follows the changes and variations in the system on a continuous and smooth surface. Running the model to adaptively gain the optimum values of the parameters on a smooth surface would facilitate further improvements in the application of other derivative based optimization control algorithms such as MPC or robust control algorithms to achieve a combined modeling-control scheme. Compared to the earlier works on the smooth fuzzy modeling structures, we could reach a desired trade-off between the model optimality and the computational load. The proposed method has been evaluated on a test problem as well as the non-linear dynamic of a chemical process.

Asymptotically log Fano pairs were introduced by Cheltsov and Rubinstein, generalising a definition of Maeda. They have received attention in the last decade within the theory of K-stability, as they approximate log Calabi Yau pairs while staying in the log Fano setting. In this note, written for the ZAG Proceedings, we summarise our talk on 1st September 2020 (Day of Knowledge) given on Zoom during the 24-hour ZAG Marathon. In the talk, we reported on joint work with P. Cascini and Y. Rubinstein, on the classification of the two-dimensional case, known as asymptotically log del Pezzo pairs., Comment: For publication on the ZAG seminar proceedings. 3 pages

We study the K-moduli of log del Pezzo pairs formed by a del Pezzo surface of degree $d$ and an anti-canonical divisor. These moduli spaces naturally depend on one parameter, providing a natural problem in variations of K-moduli spaces. For degrees 2, 3, 4, we establish an isomorphism between the K-moduli spaces and variations of Geometric Invariant Theory compactifications, which generalizes the isomorphisms in the absolute cases established by Odaka--Spotti--Sun and Mabuchi--Mukai., Comment: 41 pages, 6 tables. Comments are very welcome!

The integration of magnetic nanoparticles within fibrillar structures represents an interesting avenue for the remotely controlled release of therapeutic agents. This work presents a novel drug release platform based on electrospun magnetic fibers (EMFs) combining drugs, magnetic nanoparticles (MNPs) and mesoporous silica nanoparticles (MSNs) for controlled drug delivery via alternating magnetic fields (AMF). The platform was demonstrated to be versatile and effective for hydrophilic ketorolac (KET) and hydrophobic curcumin (CUR) encapsulation and the major response observed for AMF-triggered release was reached using drug-loaded MSNs within the fibers, providing fine control over drug release patterns. The EMFs exhibited excellent inductive heating capabilities, showing a temperature increase up to 8 {\deg}C within a 5 min AMF pulse. The system is shown to be promising for applications like transdermal pain management, oncological drug delivery, tissue engineering, and wound healing, enabling precise control over drug release in both spatial and temporal dimensions. The findings of this study offer valuable insights into the development of the next generation of smart drug delivery systems, based in multifunctional materials that can be remotely regulated and potentially revolutionize the field of nanomedicine.

We introduce a new subclass of Fano varieties (Casagrande-Druel varieties), that are $n$-dimensional varieties constructed from Fano double covers of dimension $n-1$. We conjecture that a Casagrande-Druel variety is K-polystable if the double cover and its base space are K-polystable. We prove this for smoothable Casagrande-Druel threefolds, and for Casagrande-Druel varieties constructed from double covers of $\mathbb{P}^{n-1}$ ramified over smooth hypersurfaces of degree $2d$ with $n>d>\frac{n}{2}>1$. As an application, we describe the connected components of the K-moduli space parametrizing smoothable K-polystable Fano threefolds in the families 3.9 and 4.2 in the Mori-Mukai classification., Comment: 58 pages

By identifying K-polystable limits in 4 specific deformations families of smooth Fano 3-folds, we complete the classification of one-dimensional components in the K-moduli space of smoothable Fano 3-folds., Comment: 30 pages

Acting as a goalkeeper in a video-game, a participant is asked to predict the successive choices of the penalty taker. The sequence of choices of the penalty taker is generated by a stochastic chain with memory of variable length. It has been conjectured that the probability distribution of the response times is a function of the specific sequence of past choices governing the algorithm used by the penalty taker to make his choice at each step. We found empirical evidence that besides this dependence, the distribution of the response times depends also on the success or failure of the previous prediction made by the participant. Moreover, we found statistical evidence that this dependence propagates up to two steps forward after the prediction failure.

We describe three algorithms to determine the stable, semistable, and torus-polystable loci of the GIT quotient of a projective variety by a reductive group. The algorithms are efficient when the group is semisimple. By using an implementation of our algorithms for simple groups, we provide several applications to the moduli theory of algebraic varieties, including the K-moduli of algebraic varieties, the moduli of algebraic curves and the Mukai models of the moduli space of curves for low genus. We also discuss a number of potential improvements and some natural open problems arising from this work., Comment: 32 pages, 3 figures. 1 table

We classify non-factorial nodal Fano threefolds with $1$ node and class group of rank $2$., Comment: 14 pages. Minor changes; improved exposition

This work reports on the synthesis and characterization of a new type of electrospun magnetic nanofibers (MNFs), and their application for degradation of organic pollutants using remote magnetic inductive heating. We describe a simple protocol combining a fast (app. 5 min) synthesis of MnFe\textsubscript{2}O\textsubscript{4} magnetic nanoparticles (MNPs) by sonochemical route, optimized for inductive heating, with their subsequent incorporation in electrospun MNFs composed of polyacrylonitrile (PAN) nanofibers. The resulting multifunctional MNFs (average diameter $\phi = 760 \pm 150$ nm) contain up to $\approx 30%$wt. of the MNPs. The composite showed superhydrophobic behaviour ($\theta_c = 165^\circ$) and a band gap value of 1.75 eV. We found that the presence of MNPs embedded into the polymeric nanofibers modify the exothermic and the glass transitions temperatures compared with pure PAN nanofibers, suggesting a strong attachment between MNPs and polymeric chains. The MNFs could be remotely activated by alternating magnetic fields (AMF, $f = 200-800$ kHz, $H_0 = 10-36$ kA/m) for accelerating the catalytic reactions of the organic dye methylene blue (MB). A remarkable stability of the MNFs against degradation under extreme pH conditions ($380%$ in the presence of hydrogen peroxide under AMFs, attributed to Fe\textsuperscript{2+}/\textsuperscript{3+} and Mn\textsuperscript{2+}/\textsuperscript{3+}/\textsuperscript{4+} active centers on the surface of the MNP/MNFs observed from XPS data. The capacity of these materials for magnetic remote activation appeals catalytic applications under conditions of darkness or restrained access, where no photocatalytic reactions can be achieved., Comment: 31 pages, 16 figures

In this article we address two related issues on the learning of probabilistic sequences of events. First, which features make the sequence of events generated by a stochastic chain more difficult to predict. Second, how to model the procedures employed by different learners to identify the structure of sequences of events. Playing the role of a goalkeeper in a video game, participants were told to predict step by step the successive directions -- left, center or right -- to which the penalty kicker would send the ball. The sequence of kicks was driven by a stochastic chain with memory of variable length. Results showed that at least three features play a role in the first issue: 1) the shape of the context tree summarizing the dependencies between present and past directions; 2) the entropy of the stochastic chain used to generate the sequences of events; 3) the existence or not of a deterministic periodic sequence underlying the sequences of events. Moreover, evidence suggests that best learners rely less on their own past choices to identify the structure of the sequences of events.

Comparative diagnostic in brain tumor evaluation makes possible to use the available information of a medical center to compare similar cases when a new patient is evaluated. By leveraging Artificial Intelligence models, the proposed system is able of retrieving the most similar cases of brain tumors for a given query. The primary objective is to enhance the diagnostic process by generating more accurate representations of medical images, with a particular focus on patient-specific normal features and pathologies. The proposed model uses Artificial Intelligence to detect patient features to recommend the most similar cases from a database. The system not only suggests similar cases but also balances the representation of healthy and abnormal features in its design. This not only encourages the generalization of its use but also aids clinicians in their decision-making processes. We conducted a comparative analysis of our approach in relation to similar studies. The proposed architecture obtains a Dice coefficient of 0.474 in both tumoral and healthy regions of the patients, which outperforms previous literature. Our proposed model excels at extracting and combining anatomical and pathological features from brain \glspl{mr}, achieving state-of-the-art results while relying on less expensive label information. This substantially reduces the overall cost of the training process. This paper provides substantial grounds for further exploration of the broader applicability and optimization of the proposed architecture to enhance clinical decision-making. The novel approach presented in this work marks a significant advancement in the field of medical diagnosis, particularly in the context of Artificial Intelligence-assisted image retrieval, and promises to reduce costs and improve the quality of patient care using Artificial Intelligence as a support tool instead of a black box system., Comment: 16 pages, 8 figures, 3 tables