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Environmental emergency, sustainability, technological innovation and cultural awareness: the contemporary “challenges” of the cultural heritage sector
No full textThe essay examines the challenges faced by the Cultural Heritage sector in light of the new environmental demands of the early 21st century. It explores the complex interplay between sustainability and heritage conservation, emphasizing the role of industrial heritage as fertile ground for developing sustainable strategies that integrate conservation with innovation. The chapter advocates the need to reconcile environmental and cultural awareness by considering heritage as an active resource for future generations and a key element in the transition to a sustainable, energy-efficient society
Emozioni e sistema nervoso: la forza della musica e il ruolo della neurofisiologia
No full textEmotions are constantly present in our daily lives, arising from external stimuli or from our thoughts and influencing our behaviour. Involuntary responses are mediated by the autonomic nervous system, while most of our actions are controlled by cortical output that we are aware of. Emotions are somewhere in between, we are not always aware of them, but they strongly influence our actions. The deepest and oldest part of our brain is involved in the generation of
emotions, connecting to all our organs and reaching the skin. Of the many emotions we can feel, anxiety is one of the most common and pervasive. It can negatively affect our cognitive performance, but music can powerfully modulate anxiety and many other emotions in both normal subjects and patients. Sympathetic skin response is a neurophysiological technique capable of detecting changes in skin impedande due to involuntary brief production of sweat as our autonomic nervous system follows emotions. This technique can demonstrate and measure the effect of music on our emotions
Turning aquaculture side-streams into industrial solutions: a possibility to promote sustainability and economic efficiency
No full textA Generative Model Approach for LiDAR-Based Classification and Ego Vehicle Localization Using Dynamic Bayesian Networks
No full textOur work presents a robust framework for classifying static and dynamic tracks and localizing an ego vehicle in dynamic environments using LiDAR data. Our methodology leverages generative models, specifically Dynamic Bayesian Networks (DBNs), interaction dictionaries, and a Markov Jump Particle Filter (MJPF), to accurately classify objects within LiDAR point clouds and localize the ego vehicle without relying on external odometry data during testing. The classification phase effectively distinguishes between static and dynamic objects with high accuracy, achieving an F1 score of 91%. The localization phase utilizes a combined dictionary approach, integrating multiple static landmarks to improve robustness, particularly during simultaneous multi-track observations and no-observation intervals. Experimental results validate the efficacy of our proposed approach in enhancing localization accuracy and maintaining consistency in diverse scenario
Towards explainable radiomics: stability and interpretability in computational models for clinical applications
No full textRadiomics has been widely used in medical imaging for various tasks, like biomarker prediction, tumor subtype classification, and forecast of disease recurrence and treatment response. However, increasing literature shows that the radiomics approach is rarely explored from a methodological perspective. This thesis focuses on key challenges of radiomic analysis, encompassing segmentation, evaluation of feature stability, construction of explicable prediction models, and image harmonization, with the main scope of investigating radiomics reliability and contributing to the development of interpretable radiomics-based models for prediction purposes in clinical applications. Radiomics reliability is addressed in terms of stability with respect to segmentation variability and stability of the predictive performance. The proposed machine learning models focus on the explainability of feature selection for the prediction of triple-negative breast cancer subtype, and on the interpretability of model architecture for radiomics-based longitudinal prediction of glioblastoma response to treatment. Other contributions concern numerical schemes for medical image segmentation and deep learning Image2Image network for image harmonization, both conceived as a preliminary step to radiomic analysis. While the investigation conducted in this thesis deals with specific applications, the methodology introduced and the findings reported could potentially provide a broader understanding of the principles underlying radiomics-based analyses. Ultimately, this work aspires to contribute to a deeper comprehension of the interplay between feature stability and segmentation variability for model explainability. Moreover, the construction of interpretable models and harmonization strategies represents a step toward refining the radiomics workflow, fostering the development of more reliable, generalizable, and clinically applicable computational models for diagnosis and prognosis
Antibiotic Prescription for the Prevention of Postoperative Complications After Third-Molar Extractions: A Systematic Review
No full textAbstract: Background: Third-molar extractions are common procedures often complicated
by infections and alveolitis. The use of antibiotics as prophylaxis to prevent these complica-
tions is debated due to potential risks and side effects. Therefore, the aim of the present
systematic review was to determine the efficacy of antibiotic prescription for the prevention
of these complications. Methods: A comprehensive literature search was conducted in
MEDLINE/PubMed, Cochrane, and SCOPUS databases up until June 2024. The focused
question was “Does the antibiotic prescription influence the incidence of postoperative
complications following third-molar extractions in healthy patients?” Systematic reviews
assessing complications after third-molar extractions were included. Results: A total of
16 studies were included, revealing that antibiotic use significantly reduces infection risk
and dry socket incidence compared to no prescription. Amoxicillin–clavulanic acid was
particularly effective. Conclusions: Antibiotics, especially amoxicillin–clavulanic acid, are
effective in preventing postoperative infections and alveolitis after third-molar extraction.
However, their administration should be carefully considered to balance benefits against
potential risks. Evidence supports the judicious use of antibiotics in dental surgery to
optimize patient outcomes, minimizing possible adverse effects and the risk of developing
antibiotic resistance
La discussione matematica nella scuola primaria. Discutere per riflettere sulla complessità di un processo di stima
No full textMeasurement of the associated production of a top-antitop-quark pair and a Higgs boson decaying into a bb pair in pp collisions at sqrt(s)=13 TeV using the ATLAS detector at the LHC
No full textThis paper reports the measurement of Higgs boson production in association with a t (t) over bar pair in the H -> b (b) over bar decay channel. The analysis uses 140 fb(-1) of 13 TeV proton-proton collision data collected with the ATLAS detector at the Large Hadron Collider. The final states with one or two electrons or muons are employed. An excess of events over the expected background is found with an observed (expected) significance of 4.6 (5.4) standard deviations. The t (t) over barH cross-section is sigma(t (t) over barH)=411(-92)(+101)fb=411 +/- 54(stat.)(-75)(+85)(syst.)fb for a Higgs boson mass of 125.09 GeV, consistent with the prediction of the Standard Model of 507(-50)(+35) fb. The cross-section is also measured differentially in bins of the Higgs boson transverse momentum within the simplified template cross-section framework
Innovazione Digitale ed Organizzativa nelle imprese artigiane
No full textIl panorama economico contemporaneo impone alle imprese artigiane una profonda riflessione sulle proprie strategie organizzative e sugli strumenti tecnologici necessari per rimanere competitive. Questo report, basato sia su dati regionali ottenuti tramite la Dashboard di Liguria Ricerche che su dati derivanti da un appostito questionario proposto
alle imprese artigiane genovesi, esplora il complesso rapporto tra il settore artigianale e l’innovazione digitale.
Le imprese artigiane rappresentano un pilastro fondamentale dell’economia italiana, custodi di competenze e tradizioni che costituiscono un patrimonio culturale inestimabile. Tuttavia, in un mercato sempre più dominato dalla digitalizzazione e dall’automazione, queste realtà si trovano ad affrontare sfide senza precedenti che richiedono non solo l’adozione di nuove tecnologie, ma anche una profonda trasformazione organizzativa.
La ricerca presentata in questo documento si basa su un’indagine condotta su 121 imprese artigiane, con l’obiettivo di comprendere i fabbisogni formativi, le competenze presenti e quelle necessarie per affrontare la transizione digitale. Particolare attenzione viene dedicata al ruolo dell’Intelligenza Artificiale come potenziale strumento di innovazione e
all’importanza della bilateralità nel supportare questo processo di trasformazione. Più precisamente, i dati provenienti dal questionario sono stati analizzati tenendo conto di due tipologie di cluster differenti: il fatturato e il numero di addetti nell’impresa.
Questo studio si propone di offrire una panoramica completa sullo stato attuale dell’innovazione digitale nel settore artigianale, identificando criticità, opportunità e possibili strategie di intervento per un futuro in cui tradizione e innovazione possano coesistere e rafforzarsi reciprocamente
My Silo Dreams. Tipologia industriale nell’immaginario grafico e progettuale ii Erich Mendelsohn
No full textIn 1924, architect Erich Mendelsohn travelled to Buffalo, New York – the world’s largest grain port at the time – to photograph and sketch the monumental grain elevators.
His interest in these industrial structures was first sparked by Walter Gropius’s 1911
lecture Monumentale Kunst und Industriebau, which introduced German architects
to American industrial architecture. While most European modernists continued to
engage with these buildings only through photos or drawings, Mendelsohn was the
only one among them who witnessed these structures firsthand. This direct encounter deeply shaped his visual and conceptual vocabulary, offering an alternative to the ornamental traditions of 19th-century architecture.
Mendelsohn’s imaginary sketches distill the dynamic forms of industrial silos into
visionary compositions, revealing their typological potential. Merging 19th-century
engineering influences with futurist ideals, his work reflects a shift toward an
architecture shaped by process, functionality, and hybrid typologies. Decades later,
Reyner Banham noted the paradox of silos in modernism: praised for their form but
misunderstood in function. Yet, in Mendelsohn’s hands, they became models of a new architectural sensitivity, defined not by static typologies but by variation, modularity, and openness to transformation.
Far from rigid classifications, Mendelsohn’s depictions of silos reveal a typological
fluidity rooted in adaptation and abstraction. Within this discourse, the grain elevator – originating as a purely functional and anonymous structure – stands as a key example of port architectural typology.
This contribution explores the grain elevator as a generative typological device.
Contemporary port silos reflect this legacy through standardized, repeatable elements that allow adaptability. Their hybrid nature challenges fixed typologies, blending infrastructure, machinery, and industrial architecture into a continuously evolving formal and spatial language