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  1. Article ; Online: Pomegranate fruit juice adulteration with apple juice: detection by UV-visible spectroscopy combined with multivariate statistical analysis.

    Pappalardo, Lucia

    Scientific reports

    2022  Volume 12, Issue 1, Page(s) 5151

    Abstract: Pomegranate is rich in high value nutritional substances known to be beneficial against several diseases and its use in medicine is known since ancient times. Due to its properties and delicious taste, pomegranate fresh fruit juices demand has been ... ...

    Abstract Pomegranate is rich in high value nutritional substances known to be beneficial against several diseases and its use in medicine is known since ancient times. Due to its properties and delicious taste, pomegranate fresh fruit juices demand has been growing worldwide and its adulteration is becoming a problem. Low-cost, user friendly and fast detection methods are therefore desirable in order to easily and rapidly detect adulteration of short shelf-life fresh fruit juices. For this purpose fresh squeezed arils pomegranate juice samples adulterated with less expensive apple juice concentrate were investigated by UV-visible spectroscopy combined with multivariate statistical analysis. Unsupervised principle component analysis (PCA), supervised projection to latent structure discriminant analysis (PLS-DA) and orthogonal projection to latent structure discriminant analysis (OPLS-DA) were performed on the full spectra. OPLS-DA analysis of UV-visible spectra proved to be a suitable method to detect pomegranate juices adulterated by more than 20% v/v apple juice concentrate.
    MeSH term(s) Fruit/chemistry ; Fruit and Vegetable Juices ; Malus ; Pomegranate ; Spectrophotometry, Ultraviolet
    Language English
    Publishing date 2022-03-25
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-022-07979-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Future directions in human mobility science.

    Pappalardo, Luca / Manley, Ed / Sekara, Vedran / Alessandretti, Laura

    Nature computational science

    2023  Volume 3, Issue 7, Page(s) 588–600

    Abstract: We provide a brief review of human mobility science and present three key areas where we expect to see substantial advancements. We start from the mind and discuss the need to better understand how spatial cognition shapes mobility patterns. We then move ...

    Abstract We provide a brief review of human mobility science and present three key areas where we expect to see substantial advancements. We start from the mind and discuss the need to better understand how spatial cognition shapes mobility patterns. We then move to societies and argue the importance of better understanding new forms of transportation. We conclude by discussing how algorithms shape mobility behavior and provide useful tools for modelers. Finally, we discuss how progress on these research directions may help us address some of the challenges our society faces today.
    MeSH term(s) Humans ; Cognition ; Transportation
    Language English
    Publishing date 2023-07-03
    Publishing country United States
    Document type Journal Article ; Review
    ISSN 2662-8457
    ISSN (online) 2662-8457
    DOI 10.1038/s43588-023-00469-4
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Mobility constraints in segregation models.

    Gambetta, Daniele / Mauro, Giovanni / Pappalardo, Luca

    Scientific reports

    2023  Volume 13, Issue 1, Page(s) 12087

    Abstract: Since the development of the original Schelling model of urban segregation, several enhancements have been proposed, but none have considered the impact of mobility constraints on model dynamics. Recent studies have shown that human mobility follows ... ...

    Abstract Since the development of the original Schelling model of urban segregation, several enhancements have been proposed, but none have considered the impact of mobility constraints on model dynamics. Recent studies have shown that human mobility follows specific patterns, such as a preference for short distances and dense locations. This paper proposes a segregation model incorporating mobility constraints to make agents select their location based on distance and location relevance. Our findings indicate that the mobility-constrained model produces lower segregation levels but takes longer to converge than the original Schelling model. We identified a few persistently unhappy agents from the minority group who cause this prolonged convergence time and lower segregation level as they move around the grid centre. Our study presents a more realistic representation of how agents move in urban areas and provides a novel and insightful approach to analyzing the impact of mobility constraints on segregation models. We highlight the significance of incorporating mobility constraints when policymakers design interventions to address urban segregation.
    Language English
    Publishing date 2023-07-26
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-023-38519-6
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article: Understanding peace through the world news.

    Voukelatou, Vasiliki / Miliou, Ioanna / Giannotti, Fosca / Pappalardo, Luca

    EPJ data science

    2022  Volume 11, Issue 1, Page(s) 2

    Abstract: Peace is a principal dimension of well-being and is the way out of inequity and violence. Thus, its measurement has drawn the attention of researchers, policymakers, and peacekeepers. During the last years, novel digital data streams have drastically ... ...

    Abstract Peace is a principal dimension of well-being and is the way out of inequity and violence. Thus, its measurement has drawn the attention of researchers, policymakers, and peacekeepers. During the last years, novel digital data streams have drastically changed the research in this field. The current study exploits information extracted from a new digital database called Global Data on Events, Location, and Tone (GDELT) to capture peace through the Global Peace Index (GPI). Applying predictive machine learning models, we demonstrate that news media attention from GDELT can be used as a proxy for measuring GPI at a monthly level. Additionally, we use explainable AI techniques to obtain the most important variables that drive the predictions. This analysis highlights each country's profile and provides explanations for the predictions, and particularly for the errors and the events that drive these errors. We believe that digital data exploited by researchers, policymakers, and peacekeepers, with data science tools as powerful as machine learning, could contribute to maximizing the societal benefits and minimizing the risks to peace.
    Supplementary information: The online version contains supplementary material available at 10.1140/epjds/s13688-022-00315-z.
    Language English
    Publishing date 2022-01-21
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 2705691-0
    ISSN 2193-1127
    ISSN 2193-1127
    DOI 10.1140/epjds/s13688-022-00315-z
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Book ; Online: One-Shot Traffic Assignment with Forward-Looking Penalization

    Cornacchia, Giuliano / Nanni, Mirco / Pappalardo, Luca

    2023  

    Abstract: Traffic assignment (TA) is crucial in optimizing transportation systems and consists in efficiently assigning routes to a collection of trips. Existing TA algorithms often do not adequately consider real-time traffic conditions, resulting in inefficient ... ...

    Abstract Traffic assignment (TA) is crucial in optimizing transportation systems and consists in efficiently assigning routes to a collection of trips. Existing TA algorithms often do not adequately consider real-time traffic conditions, resulting in inefficient route assignments. This paper introduces METIS, a cooperative, one-shot TA algorithm that combines alternative routing with edge penalization and informed route scoring. We conduct experiments in several cities to evaluate the performance of METIS against state-of-the-art one-shot methods. Compared to the best baseline, METIS significantly reduces CO2 emissions by 18% in Milan, 28\% in Florence, and 46% in Rome, improving trip distribution considerably while still having low computational time. Our study proposes METIS as a promising solution for optimizing TA and urban transportation systems.
    Keywords Computer Science - Multiagent Systems
    Publishing date 2023-06-23
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Extended Energy-Expenditure Model in Soccer: Evaluating Player Performance in the Context of the Game.

    Skoki, Arian / Rossi, Alessio / Cintia, Paolo / Pappalardo, Luca / Štajduhar, Ivan

    Sensors (Basel, Switzerland)

    2022  Volume 22, Issue 24

    Abstract: Every soccer game influences each player's performance differently. Many studies have tried to explain the influence of different parameters on the game; however, none went deeper into the core and examined it minute-by-minute. The goal of this study is ... ...

    Abstract Every soccer game influences each player's performance differently. Many studies have tried to explain the influence of different parameters on the game; however, none went deeper into the core and examined it minute-by-minute. The goal of this study is to use data derived from GPS wearable devices to present a new framework for performance analysis. A player's energy expenditure is analyzed using data analytics and K-means clustering of low-, middle-, and high-intensity periods distributed in 1 min segments. Our framework exhibits a higher explanatory power compared to usual game metrics (e.g., high-speed running and sprinting), explaining 45.91% of the coefficient of variation vs. 21.32% for high-, 30.66% vs. 16.82% for middle-, and 24.41% vs. 19.12% for low-intensity periods. The proposed methods enable deeper game analysis, which can help strength and conditioning coaches and managers in gaining better insights into the players' responses to various game situations.
    Language English
    Publishing date 2022-12-14
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s22249842
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: A Narrative Review for a Machine Learning Application in Sports: An Example Based on Injury Forecasting in Soccer.

    Rossi, Alessio / Pappalardo, Luca / Cintia, Paolo

    Sports (Basel, Switzerland)

    2021  Volume 10, Issue 1

    Abstract: In the last decade, the number of studies about machine learning algorithms applied to sports, e.g., injury forecasting and athlete performance prediction, have rapidly increased. Due to the number of works and experiments already present in the state-of- ...

    Abstract In the last decade, the number of studies about machine learning algorithms applied to sports, e.g., injury forecasting and athlete performance prediction, have rapidly increased. Due to the number of works and experiments already present in the state-of-the-art regarding machine-learning techniques in sport science, the aim of this narrative review is to provide a guideline describing a correct approach for training, validating, and testing machine learning models to predict events in sports science. The main contribution of this narrative review is to highlight any possible strengths and limitations during all the stages of model development, i.e., training, validation, testing, and interpretation, in order to limit possible errors that could induce misleading results. In particular, this paper shows an example about injury forecaster that provides a description of all the features that could be used to predict injuries, all the possible pre-processing approaches for time series analysis, how to correctly split the dataset to train and test the predictive models, and the importance to explain the decision-making approach of the white and black box models.
    Language English
    Publishing date 2021-12-24
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2704239-X
    ISSN 2075-4663 ; 2075-4663
    ISSN (online) 2075-4663
    ISSN 2075-4663
    DOI 10.3390/sports10010005
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: A Deep Gravity model for mobility flows generation.

    Simini, Filippo / Barlacchi, Gianni / Luca, Massimilano / Pappalardo, Luca

    Nature communications

    2021  Volume 12, Issue 1, Page(s) 6576

    Abstract: The movements of individuals within and among cities influence critical aspects of our society, such as well-being, the spreading of epidemics, and the quality of the environment. When information about mobility flows is not available for a particular ... ...

    Abstract The movements of individuals within and among cities influence critical aspects of our society, such as well-being, the spreading of epidemics, and the quality of the environment. When information about mobility flows is not available for a particular region of interest, we must rely on mathematical models to generate them. In this work, we propose Deep Gravity, an effective model to generate flow probabilities that exploits many features (e.g., land use, road network, transport, food, health facilities) extracted from voluntary geographic data, and uses deep neural networks to discover non-linear relationships between those features and mobility flows. Our experiments, conducted on mobility flows in England, Italy, and New York State, show that Deep Gravity achieves a significant increase in performance, especially in densely populated regions of interest, with respect to the classic gravity model and models that do not use deep neural networks or geographic data. Deep Gravity has good generalization capability, generating realistic flows also for geographic areas for which there is no data availability for training. Finally, we show how flows generated by Deep Gravity may be explained in terms of the geographic features and highlight crucial differences among the three considered countries interpreting the model's prediction with explainable AI techniques.
    Language English
    Publishing date 2021-11-12
    Publishing country England
    Document type Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, Non-U.S. Gov't
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-021-26752-4
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Exploring microstructure and petrophysical properties of microporous volcanic rocks through 3D multiscale and super-resolution imaging.

    Buono, Gianmarco / Caliro, Stefano / Macedonio, Giovanni / Allocca, Vincenzo / Gamba, Federico / Pappalardo, Lucia

    Scientific reports

    2023  Volume 13, Issue 1, Page(s) 6651

    Abstract: Digital rock physics offers powerful perspectives to investigate Earth materials in 3D and non-destructively. However, it has been poorly applied to microporous volcanic rocks due to their challenging microstructures, although they are studied for ... ...

    Abstract Digital rock physics offers powerful perspectives to investigate Earth materials in 3D and non-destructively. However, it has been poorly applied to microporous volcanic rocks due to their challenging microstructures, although they are studied for numerous volcanological, geothermal and engineering applications. Their rapid origin, in fact, leads to complex textures, where pores are dispersed in fine, heterogeneous and lithified matrices. We propose a framework to optimize their investigation and face innovative 3D/4D imaging challenges. A 3D multiscale study of a tuff was performed through X-ray microtomography and image-based simulations, finding that accurate characterizations of microstructure and petrophysical properties require high-resolution scans (≤ 4 μm/px). However, high-resolution imaging of large samples may need long times and hard X-rays, covering small rock volumes. To deal with these limitations, we implemented 2D/3D convolutional neural network and generative adversarial network-based super-resolution approaches. They can improve the quality of low-resolution scans, learning mapping functions from low-resolution to high-resolution images. This is one of the first efforts to apply deep learning-based super-resolution to unconventional non-sedimentary digital rocks and real scans. Our findings suggest that these approaches, and mainly 2D U-Net and pix2pix networks trained on paired data, can strongly facilitate high-resolution imaging of large microporous (volcanic) rocks.
    Language English
    Publishing date 2023-04-24
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-023-33687-x
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Explaining the difference between men's and women's football.

    Pappalardo, Luca / Rossi, Alessio / Natilli, Michela / Cintia, Paolo

    PloS one

    2021  Volume 16, Issue 8, Page(s) e0255407

    Abstract: Women's football is gaining supporters and practitioners worldwide, raising questions about what the differences are with men's football. While the two sports are often compared based on the players' physical attributes, we analyze the spatio-temporal ... ...

    Abstract Women's football is gaining supporters and practitioners worldwide, raising questions about what the differences are with men's football. While the two sports are often compared based on the players' physical attributes, we analyze the spatio-temporal events during matches in the last World Cups to compare male and female teams based on their technical performance. We train an artificial intelligence model to recognize if a team is male or female based on variables that describe a match's playing intensity, accuracy, and performance quality. Our model accurately distinguishes between men's and women's football, revealing crucial technical differences, which we investigate through the extraction of explanations from the classifier's decisions. The differences between men's and women's football are rooted in play accuracy, the recovery time of ball possession, and the players' performance quality. Our methodology may help journalists and fans understand what makes women's football a distinct sport and coaches design tactics tailored to female teams.
    MeSH term(s) Artificial Intelligence ; Athletes ; Brain Concussion ; Soccer ; Universities
    Language English
    Publishing date 2021-08-04
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0255407
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