The increasing availability of traffic data from sensor networks has created new opportunities for understanding vehicular dynamics and identifying anomalies. In this study, we employ clustering techniques to analyse traffic flow data with the dual objective of uncovering meaningful traffic patterns and detecting anomalies, including sensor failures and irregular congestion events. We explore multiple clustering approaches, i.e. partitioning and hierarchical methods, combined with various time series representations and similarity measures. Our methodology is applied to real-world data from highway sensors, enabling us to assess the impact of different clustering frameworks on traffic pattern recognition. We also introduce a clustering-driven anomaly detection methodology that identifies deviations from expected traffic behaviour based on distance-based anomaly scores. Results indicate that hierarchical clustering with symbolic representations provides robust segmentation of traffic patterns, while partitioning methods such as k-means and fuzzy c-means yield meaningful results when paired with Dynamic Time Warping. The proposed anomaly detection strategy successfully identifies sensor malfunctions and abnormal traffic conditions with minimal false positives, demonstrating its practical utility for real-time monitoring. Real-world vehicular traffic data are provided by Autostrade Alto Adriatico S.p.A.

The study at the Peggy Guggenheim Collection in Venice highlights critical interactions between indoor air quality, visitor dynamics, and microclimatic conditions, offering insights into preventive conservation of modern artworks. By analyzing pollutants such as ammonia, formaldehyde, and organic acids, alongside visitor density and environmental data, the research identified key patterns and risks. Through three seasonal monitoring campaigns, the concentrations of SO2 (sulphur dioxide), NO (nitric oxide), NO2 (nitrogen dioxide), NOx (nitrogen oxides), HONO (nitrous acid), HNO3 (nitric acid), O3 (ozone), NH3 (ammonia), CH3COOH (acetic acid), HCOOH (formic acid), and HCHO (formaldehyde) were determined using passive samplers, as well as temperature and relative humidity data loggers. In addition, two specific short-term monitoring campaigns focused on NH3 were performed to evaluate the influence of visitor presence on indoor concentrations of the above compounds and environmental parameters. NH3 and HCHO concentrations spiked during high visitor occupancy, with NH3 levels doubling in crowded periods. Short-term NH3 campaigns confirmed a direct correlation between visitor numbers and the above indoor concentrations, likely due to human emissions (e.g., sweat, breath) and off-gassing from materials. The indoor/outdoor ratios indicated that several pollutants originated from indoor sources, with ammonia and acetic acid showing the highest indoor concentrations. By measuring the number of visitors and microclimate parameters (temperature and humidity) every 3 s, we were able to precisely estimate the causality and the temporal shift between these quantities, both at small time scale (a few minute delay between peaks) and at medium time scale (daily average conditions due to the continuous inflow and outflow of visitors).

In this paper we propose a novel macroscopic (fluid dynamics) model for describing pedestrian flow in low and high density regimes. The model is characterized by the fact that the maximal density reachable by the crowd – usually a fixed model parameter – is instead a state variable. To do that, the model couples a conservation law, devised as usual for tracking the evolution of the crowd density, with a Burgers-like PDE with a nonlocal term describing the evolution of the maximal density. The variable maximal density is used here to describe the effects of the psychological/physical pushing forces which are observed in crowds during competitive or emergency situations. Specific attention is also dedicated to the fundamental diagram, i.e., the function which expresses the relationship between crowd density and flux. Although the model needs a well defined fundamental diagram as known input parameter, it is not evident a priori which relationship between density and flux will be actually observed, due to the time-varying maximal density. An a posteriori analysis shows that the observed fundamental diagram has an elongated “tail” in the congested region, thus resulting similar to the concave/concave fundamental diagram with a “double hump” observed in real crowds. The main features of the model are investigated through 1D and 2D numerical simulations. The numerical code for the 1D simulation is freely available on this Gitlab repository.

In this work, we deal with a mathematical model describing the dissolution process of irregularly shaped particles. In particular, we consider a complete dissolution model accounting for surface kinetics, convective diffusion, and relative velocity between fluid and dissolving particles, for three drugs with different solubility and wettability: theophylline, griseofulvin, and nimesulide. The possible subsequent recrystallization process in the bulk fluid is also considered. The governing differential equations are revisited in the context of the level-set method and Hamilton-Jacobi equations, then they are solved numerically. This choice allows us to deal with the simultaneous dissolution of hundreds of different polydisperse particles. We show the results of many computer simulations which investigate the impact of the particle size, shape, area/volume ratio, and the dependence of the interfacial mass transport coefficient on the surface curvature.

In this paper, we derive a kinetic description of swarming particle dynamics in an interacting multi-agent system featuring emerging leaders and followers. Agents are classically characterized by their position and velocity plus a continuous parameter quantifying their degree of leadership. The microscopic processes ruling the change of velocity and degree of leadership are independent, non-conservative and non-local in the physical space, so as to account for long-range interactions. Out of the kinetic description, we obtain then a macroscopic model under a hydrodynamic limit reminiscent of that used to tackle the hydrodynamics of weakly dissipative granular gases, thus relying in particular on a regime of small non-conservative and short-range interactions. Numerical simulations in one- and two-dimensional domains show that the limiting macroscopic model is consistent with the original particle dynamics and furthermore can reproduce classical emerging patterns typically observed in swarms.

Nel cuore della scuola primaria, luogo privilegiato per la costruzione delle conoscenze di base e per lo sviluppo delle prime competenze trasversali, si colloca il progetto STI2MA, acronimo di Scienza, Tecnica, Ingegno, Italiano, Matematica e Arte. Si tratta di una proposta strutturata di rinnovamento della didattica della matematica in chiave interdisciplinare e sostenibile, nel solco delle Indicazioni Nazionali e dei più recenti orientamenti internazionali in materia di educazione alla cittadinanza globale. L’autrice mette a punto un curricolo integrato che muove dall’idea, tanto Montessoriana quanto di epistemologia scientifica contemporanea, secondo cui la matematica non è solo una disciplina ma un linguaggio per pensare, per interpretare il mondo, per agire con consapevolezza, per riflettere e per scoprire tutte le dimensioni del proprio universo culturale. L’autrice mette a punto un curricolo integrato che muove dall’idea, tanto Montessoriana quanto di epistemologia scientifica contemporanea, secondo cui la matematica non è solo una disciplina ma un linguaggio per pensare, per interpretare il mondo, per agire con consapevolezza, per riflettere e per scoprire tutte le dimensioni del proprio universo culturale.

il presente rapporto mira a esplorare come le nuove modalità di lavoro flessibile, favorite dalla digitalizzazione, possano promuovere un equilibrio virtuoso tra vita professionale e privata, migliorando al contempo le performance e il benessere individuale e organizzativo. Attraverso l'analisi della letteratura e del quadro normativo, si esamina il concetto di work-life balance, delineandone vantaggi e criticità. Si indaga, poi, come il benessere organizzativo si traduca in risultati concreti sia per i lavoratori che per le organizzazioni, creando un circolo virtuoso che lega la salute psico-fisica dei dipendenti a una maggiore produttività, efficienza, efficacia e sostenibilità nel lungo periodo, con un focus sul settore pubblico. Viene dedicata particolare attenzione all'impatto della digitalizzazione come fattore chiave per l'implementazione di politiche di work-life balance, analizzando sia le opportunità offerte dagli strumenti per una maggiore flessibilità sia le criticità legate ai confini sempre più sfumati tra le due sfere di vita. Le conclusioni sintetizzano i principali risultati emersi, offrendo una riflessione sulle sfide e le opportunità che caratterizzano il futuro del lavoro, delineando possibili prospettive future.

Human behavior is a key determinant of epidemic outcomes. During health crises, variations in people's responses to control measures, often driven by different levels of risk perception, lead to variability in epidemic parameters such as infectiousness and susceptibility. We introduce a model within the Susceptible-Infected-Removed (SIR) class that accounts for these heterogeneities. We find that there is a region in the space of parameters just above the epidemic threshold, where trajectories showing an initial decline in the number of Infected can suddenly reverse and give rise to widespread transmission. Such heterogeneity can lead to an underestimation of transmission potential and delayed recognition of epidemic resurgence, thereby severely compromising efforts for a timely response. We examine this phenomenon in the mean-field scenario and then simulate the dynamics on homogeneous and heterogeneous contact networks, confirming that this phenomenology persists beyond mean field. Our model also encompasses cases where the heterogeneity originates from biological or other factors.

Motivation The rapid expansion of single-cell RNA sequencing (scRNA-seq) technologies has increased the need for robust and scalable clustering evaluation methods. To address these challenges, we developed robin2, an optimized version of our R package robin. It introduces enhanced computational efficiency, support for high-dimensional datasets, and harmonious integration with R's base functionalities for robust network analysis.Results robin2 offers improved functionality for clustering stability validation and enables systematic evaluation of community detection algorithms across various resolutions and pipelines. The application to Tabula Muris and PBMC scRNA-seq datasets confirmed its ability to identify biologically meaningful cell subpopulations with high statistical significance. The new version reduces computational time by 9-fold on large-scale datasets using parallel processing.Availability and implementation The robin2 package is freely available on CRAN at https://CRAN.R-project.org/package=robin. Comprehensive documentation and a detailed analysis vignette are available on GitHub at https://drighelli.github.io/scrobinv2/index.html.

The diagnosis and monitoring of eosinophilic esophagitis (EoE), a common pediatric pathology, typically involves invasive procedures such as an upper endoscopy with biopsies, imposing a significant burden on patients and healthcare systems. We aimed to assess miR-21-5p and miR-223-3p levels in pediatric EoE patients and evaluate their as potential non-invasive biomarkers of disease activity and response to treatments. We enrolled 13 children with EoE and 8 controls. Plasma and esophageal mucosa samples from patients were collected at diagnosis and after 8-10 weeks of therapy and compared with control samples. After microRNA(miRNA) extraction, the levels of miR-21-5p and miR-223-3p and their relevant target genes were analyzed. Bioinformatic analysis was used to identify the predicted target genes and pathways that are potentially relevant for disease pathophysiology. Plasma levels of miR-21-5p and miR-223-3p were significantly higher in EoE patients than in the controls, reflecting their levels in esophageal mucosa. The target genes of these miRNAs are involved in key signaling pathways (MAPK, Ras, and FoxO), relevant for EoE pathophysiology. Among these, STAT3 (Signal Transducer and Activator of Transcription 3) and PTEN (Phosphatase and Tensin Homolog), which are significantly downregulated in patient esophageal mucosa, are implicated in eosinophilic gastroenteropathies and autoimmune diseases. Following therapy (proton pump inhibitors and/or fluticasone propionate), plasma and tissue expression of both miRNAs significantly decreased and were no longer different from the controls. These microRNAs may serve as complementary non-invasive EoE markers and reduce the need for endoscopy/biopsies.

Engineering mammalian cells with synthetic circuits drives innovation in next-generation biotherapeutics and industrial biotechnology. However, applications often depend on cellular productivity, which is constrained by finite cellular resources. Here, we harness computational biology to identify drugs that boost productivity without additional genetic modifications. We perform RNA-sequencing on cells expressing an incoherent feed-forward loop (iFFL), a genetic circuit that enhances operational capacity. To find drugs that mimic this effect, we use DECCODE (Drug Enhanced Cell COnversion using Differential Expression), an unbiased method that matches our transcriptional data with thousands of drug-induced profiles. Among the compound candidates, we select Filgotinib, that enhances expression of both transiently and stably expressed genetic payloads across various experimental scenarios and cell lines, including AAV and lentivirus transduction. Our results reveal cell-specific responses, underscoring the context dependency of small-molecule treatments. Altogether, we present a versatile tool for biomedical and industrial applications requiring enhanced productivity from engineered cells.

In tokamaks, measuring electron temperatures in the plasma core may be quite challenging, especially when they exceed 6-8 keV [1]. Discrepancies are detected between the values measured by different diagnostics, such as Thomson Scattering (TS) and Electron Cyclotron Emission (ECE), which are expected to agree. Accurate and reliable determination of electron temperature in high-temperature scenarios, is crucial for the development of future reactors like ITER and Demo (with ITER's core plasma expected to have an electron temperature of about 25 keV) [2], as well as for the Chinese Fusion Engineering Test Reactor (CFETR) [3]), in which discrepancies in electron temperature measurements could be even more pronounced. Resolving this diagnostic issue is crucial because it implies a deep understanding of important aspects of plasma physics in the core and beyond. Recently, further studies on this topic have yielded substantial results and clarified several aspects [9,10,11,12]. Current research focuses on the possible causes of the local non-Maxwellian shape of the electron energy distribution function, which is at the root of these discrepancies [1,4,5,6,7,8,9,10,11,12]. Ongoing research by various groups working on magnetic confinement machines worldwide aims to address this long-standing issue within the framework of an ITPA (International Tokamak Physics Activity) initiative. This paper proposes a method to compare data collected by ECE and TS, which is based upon previously developed techniques for analyzing JET data [1], and is implemented in a dedicated Python code, to tackle issues identified in recent years, while also ensuring the output is comparable across different machines. The goal is to perform comparisons under consistent conditions, irrespective of machine-specific factors such as dimensions, fields, and coordinates. The methods developed and the corresponding implementations in a code, addresses several critical aspects, including the positions of measurements, i.e. the plasma position relative to the diagnostics' lines of sight (LoS), the involved volumes, and other controls to ensure uniformity of results during multi-shot analyses. These controls encompass acquisition rate, data interpolation, and the equilibrium reconstruction codes employed, with the objective of obtaining the best possible comparison between the two diagnostics.

This article is dedicated to study the physical causes of a long-standing issue experienced in different tokamak devices throughout the last decades: the observed discrepancies between electron cyclotron emission (ECE) and Thomson Scattering (TS) diagnostic measurements at high temperature in the core tokamak plasmas. A recently developed heuristic model (Fontana et al 2023 Phys. Plasmas 30 122503), tested on an extensive data set from multiple pulses in the frame of recent JET campaigns, showed that such ECE-TS discrepancy could be reconciled by introducing a bipolar perturbation in the electron distribution function. Such a perturbation indeed modifies the EC emission and absorption spectra. Nonetheless, the heuristic model does not provide the physical mechanisms causing such a bipolar perturbation. In this work, detailed gyrokinetic analyses unveil the unexplored wave-particle interaction between electrons and the Kinetic Ballooning Modes (KBMs) in tokamak plasmas. The numerical studies of the core of a selected high-temperature pulse of the JET device revealed that the electron-β was large enough to destabilize KBMs. Such KBMs affect the electron distribution function in momentum space with a characteristic bipolar structure. The position of the bipolar structure in the velocity space is intimately linked to the electron diamagnetic frequency. The amplitude of the perturbation, assessed through nonlinear computations, is shown to be dependent on the amplitude of the KBM-induced turbulent fluxes. Thus, this study demonstrates that KBMs, destabilized by the high-β plasma conditions achieved in the core of high-temperature scenarios, perturb the electron distribution function forming bipolar structures in momentum space and, thereby, modifying the EC spectrum. Therefore, the reported mechanism may represent an intriguing explanation of the ECE-TS measurement discrepancy in the deep core of high-temperature plasmas.

One of the milestones to be achieved in the design of the bolometric diagnostics for the new Italian Divertor Tokamak Test (DTT) project is the estimation of the radiated power at the start of operation, i.e. the so-called first plasma, in order to perform various tasks ranging from scientific analysis and planning of the discharges to the feedback protection of the machine. In fact, real-time (RT) feedback control of the radiation pattern for prevention is both a delicate and important matter, for example in terms of mitigating and avoiding disruptions. It would therefore be desirable to monitor not only the total power emitted, but also the one emitted by the different regions of the plasma. This paper then focuses on showing the initial design of the main strategy for estimating the plasma radiation in two different situations: for RT control and for an inter-shot analysis. The first approach for RT then, is based on the estimation of the radiated power inside the first wall using specific lines of sight (LoS). Such estimates have been compared with those obtained from slower tomographic reconstructions of synthetic emissivity profiles (phantoms). Furthermore, a first design of the Region Of Interest (ROI) for a fast implementation of an already established macro-estimation of the radiated power in different locations of the main chamber is provided and the overall method is adapted for DTT. Regarding the design of the inter-shot data exploitation then, since tomographic reconstructions will most likely be available during an inter-shot basis, it is planned to provide a more accurate estimate of the radiated power from different locations of the device for a better design and tuning of the discharges. In order to achieve such a long-term goal, an initial strategy for adapting a maximum likelihood based algorithm for inter-shot analysis is described.

On both the Joint European Torus (JET) and the ASDEX-Upgrade (AUG) tokamaks, an Expectation Maximization algorithm has been adapted to implement a Maximum Likelihood (ML) approach to derive tomograms from bolometers data. The main feature of such an approach is the ability to estimate the variance associated with the reconstructed tomograms and hence the uncertainties in the derived quantities. It has therefore been selected to support the design of the bolometric diagnostics for the Divertor Tokamak Test Facility (DTT). A reliable reconstruction of the emissivity profile is indeed relevant for the scientific exploitation of the device. As any tomographic inversion method is an ill-conditioned problem, two milestones should be achieved: ensuring the reconstruction of specific features and minimizing the risk of producing artefacts. The design strategy for the bolometric diagnostic on DTT involves testing various possible layouts to ensure that they meet the above requirements and are compatible with the engineering and machine constraints. This contribution focuses on showing how the conceptual bolometric layout can handle different phantoms mimicking typical emissivities observed on JET and AUG. A methodology has been developed to further optimize the layout within the constraints of the machine design. For the initial phase of DTT, a reduced layout is proposed, utilizing half of the conceptual lines of sight. The ability to reconstruct specific emissivity features while minimizing the risk of producing artefacts has been tested. The current ML implementation uses an anisotropic diffusion technique and is already significantly faster than the JET implementation for each reconstruction.

In the European roadmap towards nuclear fusion, the new Italian project DTT (Divertor Tokamak Test), currently under construction at the ENEA Frascati Research Centre, aims at exploring alternative solutions for the divertor, in support to the optimization of the divertor configuration foreseen for DEMO. A dedicated set of diagnostics are planned to measure the total radiated power and to monitor plasma processes by means of Soft-X emission measurements. The total radiated power acquisition will rely on commercial metal resistor bolometers, while for the SXR range of energies a new technology based on custom Chemical Vapor Deposition diamonds (CVD) will be adopted. This work focuses on the mechanical layout design of these fundamental diagnostics, respecting geometrical and functional constrains of DTT and minimizing the diagnostics volume inside the access pipe. An integrated and compact solution, allowing flexible positioning and easy maintenance of the two systems has been identified, with a structural layout based on a modular approach. The detectors of the two diagnostics are installed on a common frame, with adjustable mounts for an independent fine-tuning of their alignment. The high heat load from the plasma is coped with by means of a custom active water-cooling system, to protect the sensors while ensuring stable and reliable operations. To allow a good physical exploitation of the collected data, i.e. with tomographic reconstructions, the lines of sight of both systems must be properly arranged on the poloidal section of the plasma. The proposed mechanical design foresees a customized chassis for each of the four poloidal ports of the DTT vacuum vessel, assuring a suitable coverage of the plasma's poloidal cross-section.

Background Analysis of Electronic Health Records (EHRs) is crucial in real-world evidence (RWE), especially in oncology, as it provides valuable insights into the complex nature of the disease. The implementation of advanced techniques for automated extraction of structured information from textual data potentially enables access to expert knowledge in highly specialized contexts. In this paper, we introduce MISTIC, a Natural Language Processing (NLP) approach to classify the presence or absence of metastasis in Italian EHRs, in the breast cancer domain. Methods Our approach consists of a transformer-based framework designed for few-shot learning, requiring a small labelled dataset and minimal computational resources for training. The pipeline includes text segmentation to improve model processing and topic analysis to filter informative content, ensuring relevant input data for classification. Results MISTIC was evaluated across multiple data sources, and compared to several benchmark methodologies, ranging from a pattern-matching system, composed of regex and semantic rules, to BERT-based models implemented in a zero-shot learning setup and Large Language Models (LLMs). The results demonstrate the generalization of our approach, achieving an F-Score above 87% on all the sources, and outperforming the other experiments, with an overall F-Score of 91.2%. Conclusions MISTIC achieves high performance in the Italian metastasis classification task, outperforming rule-based systems, zero-shot BERT models, and LLMs. Its few-shot learning setup offers a computationally efficient alternative to large-scale models, while its segmentation and topic analysis steps enhance explainability by explicitly linking predictions to key textual elements. Furthermore, MISTIC demonstrates strong generalization across different data sources, reinforcing its potential as a scalable and transparent solution for clinical text classification. By extracting high-quality metastatic information from diverse textual data, MISTIC supports medical researchers in analyzing unstructured and highly informative content across a wide range of medical reports. In doing so, it enhances data accessibility and interpretability, addressing a critical gap in health informatics and clinical practice.

We

The increasing availability of clinical reports offers valuable opportunities for natural language processing (NLP) applications in healthcare. Large Language Models (LLMs), such as BERT-based architectures and generative models, have shown great promise in text classification, summarization, and semantic analysis. However, applying LLMs to Electronic Health Records (EHRs) poses challenges due to token limits and the complexity of clinical text. Sentence Boundary Detection (SBD), which segments text into meaningful units, is a critical preprocessing step to address token constraints and improve model interpretability, particularly for tasks like text classification. This study benchmarks several SBD methods, including traditional approaches (e.g., NLTK, Stanza, PySBD) and state-of-the-art transformer-based models, such as Segment Any Text (SAT), fine-tuned using low-rank adaptation (LoRA) for the clinical domain. The models were evaluated on a dataset of clinical reports in Italian, sourced from the Gemelli hospital of Rome, using metrics like F1-score to measure segmentation quality. The results reveal that PySBD achieved the best performance, closely aligning with the gold standard, with a median F1-Score of 83%. We also assessed the impact of segmentation on a downstream metastasis classification task, comparing the performance of a transformer-based model applied to unsegmented reports versus reports processed with PySBD. Segmentation outperformed the entire report scenario, with a higher F1-Score of 92% versus 88%, demonstrating that SBD improves text classification by ensuring semantic coherence, adhering to token constraints, and providing sentence-level explainability. In conclusion, this study highlights the importance of SBD in enhancing both the quality and interpretability of downstream NLP tasks in healthcare. By benchmarking traditional and transformer-based SBD models, we validate the role of segmentation as a critical preprocessing step to advance clinical NLP applications, offering insights for improving performance and clinical relevance in the processing of EHRs.

On a compact interval, we introduce and study a whole family of wavelets depending on a free parameter that can be suitably modulated to improve performance. Such wavelets arise from de la Vallée Poussin (VP) interpolation at Chebyshev nodes, generalizing previous work by Capobianco and Themistoclakis, who considered a special parameter setting. In our construction, both scaling and wavelet functions are interpolating polynomials at some Chebyshev zeros of 1st kind. Contrarily to the classical approach, they are not generated by dilations and translations of a single mother function and are naturally defined on the interval [-1,1] to which any other compact interval can be reduced. In the paper, we provide a non-standard multiresolution analysis with fast (DCT-based) decomposition and reconstruction algorithms. Moreover, we state several theoretical results, particularly on convergence, laying the foundation for future applications.