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IL DIRITTO DELL'UNIONE EUROPEA NEGLI APPALTI PUBBLICI SOTTO LE SOGLIE DI RILEVANZA COMUNITARIA
No full textLa tesi esamina l’incidenza del diritto dell’Unione europea sulla disciplina nazionale degli appalti pubblici di importo inferiore alle soglie di valore previste per l’applicazione della direttiva 2014/24/UE. Tali affidamenti, pur esclusi dalla disciplina armonizzata, rientrano nell’ambito di applicazione dei Trattati e dei principi generali del diritto dell’Unione qualora siano idonei ad attrarre l’interesse di operatori economici stabiliti in altri Stati membri (c.d. interesse transfrontaliero certo). Essi risultano pertanto soggetti, in particolare, alle libertà di circolazione e ai principi di parità di trattamento, non discriminazione, trasparenza e proporzionalità.
In assenza di criteri univoci per l’accertamento dell’interesse transfrontaliero e di indicazioni sulle modalità di attuazione di tali vincoli da parte degli Stati membri, la disciplina applicabile agli appalti sottosoglia si caratterizza oggi per un elevato grado di incertezza e per significative divergenze tra gli ordinamenti nazionali.
Muovendo dall’analisi della disciplina italiana introdotta dall’art. 48, comma 2, del d.lgs. n. 36 del 2023, la tesi propone un’evoluzione della normativa comunitaria mediante l’inserimento, nella direttiva di settore, di una disposizione specificamente dedicata agli appalti sottosoglia con interesse transfrontaliero certo. Tale norma potrebbe, da un lato, fissare criteri omogenei per la valutazione della sussistenza di tale interesse e, dall’altro, prescrivere l’applicazione selettiva delle sole norme della disciplina sopra soglia necessarie a garantire l’effettività dei principi del diritto dell’Unione.
La proposta mira a superare la frammentarietà dell’attuale assetto normativo, conducendo a un più equilibrato bilanciamento tra esigenze di semplificazione (che caratterizzano tali appalti) e applicazione effettiva delle libertà e dei principi del diritto dell'Unione, in un’ottica di rafforzamento della certezza del diritto e di maggiore uniformità nelle discipline dei diversi Stati membri.The thesis examines the impact of European Union law on the national regulation of public procurement contracts whose value falls below the thresholds established for the application of Directive 2014/24/EU. Although such contracts are excluded from the harmonised regime, they nevertheless fall within the scope of the Treaties and of the general principles of EU law where they are capable of attracting the interest of economic operators established in other Member States (so-called certain cross-border interest). They are therefore subject, in particular, to the freedoms of movement and to the principles of equal treatment, non-discrimination, transparency and proportionality.
In the absence of uniform criteria for assessing the existence of cross-border interest and of guidance on the modalities for implementing these obligations at national level, the legal framework applicable to below-threshold contracts is currently characterised by a high degree of uncertainty and by significant divergences among Member States’ legal systems.
Building on an analysis of the Italian framework introduced by Article 48(2) of Legislative Decree No. 36 of 2023, the thesis proposes an evolution of EU legislation through the insertion, within the relevant directive, of a provision specifically devoted to below-threshold contracts with certain cross-border interest. Such a provision would, on the one hand, establish harmonised criteria for assessing the existence of such interest and, on the other, prescribe the selective application of only those rules governing above-threshold contracts that are necessary to ensure the effectiveness of EU law principles.
The proposal aims to overcome the fragmentation of the current regulatory framework by promoting a more balanced relationship between the need for procedural simplification — which characterises such contracts — and the effective application of EU freedoms and principles, with a view to strengthening legal certainty and achieving greater uniformity among the national systems of the Member States
Frammenti documentari di riuso in legatura presso la Biblioteca Capitolare di Vercelli (secoli X-XV)
No full textA MULTI-STAGE MACHINE LEARNING FRAMEWORK FOR PROSTATE CANCER DIAGNOSIS BASED ON MPMRI IMAGING
No full textProstate cancer is one of the most common malignancies among men worldwide. Early and accurate diagnosis of the pathology plays a critical role in improving treatment outcomes. This thesis presents a comprehensive Machine Learning and Deep Learning ((ML)/(DL)) framework for prostate cancer diagnosis: it integrates zonal prostate segmentation, lesion segmentation, Prostate Imaging Reporting and Data System (PI-RADS) classification, and cancer detection using multi-parametric Magnetic Resonance Imaging (mp-MRI). The analysis relies on data from T2-weighted imaging (T2W), Apparent Diffusion Coefficient (ADC) imaging, and Diffusion-weighted Imaging (DWI), along with morphologic and biomarker-related clinical information. The proposed multi-stage workflow aims to support radiologists, reduce diagnostic variability, and possibly serve as an educational tool for medical trainees.
The studied Deep Learning models were based on U-Net architectures. For peripheral zone (PZ) and central gland (CG) segmentation, we relied on the Prostate158 dataset of 3T mp-MRIs: we trained on 90 scans, validated on 25, and tested on 24. We considered Attention-Res-UNet, Vanilla-Net, and V-Net individually and also as an ensemble. Meta-Net and YOLO-V8 were also evaluated. We achieved high performance in providing anatomically precise delineations: YOLO-
V8 achieved the best results, with a DSC of 89% for the central gland and 73% for peripheral zone segmentation.
For lesion segmentation, we developed a dedicated dataset of 311 mp-MRI cases collected at the Centro Diagnostico Italiano (CDI), Milan, including 58 PI-RADS 3 and 253 PI-RADS 4–5 cases. The data set comprised T2W, ADC, and DWI sequences with manually annotated masks and was preprocessed by normalization and registration. Four deep learning architectures, U-Net, Dense U-Net, Attention U-Net, and LSTM U-Net, were evaluated using both single-modality and multi-
input strategies. The Dense U-Net achieved the best performance, with a DSC of 69% on ADC images for PI-RADS 4–5 and 68% for PI-RADS 3–5.
PI-RADS is a standardized scoring system used by radiologists to categorize prostate lesions based on their likelihood of clinically significant cancer. The PI-RADS machine learning classifier improved diagnostic consistency by extracting discriminative imaging features. Three approaches were evaluated for automated PI-RADS 3–5 classification using T2W, DWI, and ADC sequences: (1) hand-crafted radiomic features from manually segmented lesions, (2) a fully automated lesion and zonal segmentation pipeline, and (3) a custom convolution neural net-
work learning high-level features from ADC images and masks. ADC-derived features performed best, with an ensemble model achieving 77% accuracy Accuracy (Acc), 83% AUC, and 0.618 Matthews Correlation Coefficient Matthews Correlation Coefficient (MCC), with PI-RADS 5 most reliably classified (AUC 94%), while PI-RADS 3 remained the most challenging.
The cancer detection stage integrated imaging and clinical parameters, such as age, prostate-specific antigen (Prostate-Specific Antigen (PSA)) levels, and biopsy results, to enhance malignancy prediction. In total, 345 patients were included in the study, with data collected from Trita Hospital in Tehran, Iran. Using ADC images, the proposed model achieved the highest performance, with an accuracy of 83%, AUC of 87%, and a Matthews Correlation Coefficient of 0.638.
A key contribution of this research is the creation of an end-to-end clinically inspired workflow that connects all diagnostic stages and includes, for practical deployment, a user-friendly 3D Slicer plugin that we contributed to developing.
Although the size of the data set and the lack of multicenter validation still represent limitations of the work, the results highlight the potential of multimodal AI-driven approaches to improve diagnostic accuracy, standardize reporting, and help personalized patient care.
This work establishes a foundation for future developments in AI-assisted prostate cancer diagnosis and clinical decision support systems
Automated machine learning for bio-oil yield prediction from lignocellulosic biomass pyrolysis
No full textLignocellulosic biomass pyrolysis for bio-oil production stands as a promising route for renewable energy, yet predicting bio-oil yield remains challenging due to the complex interplay of biomass properties and process conditions. Conventional Machine Learning (ML) approaches, while effective, require labor-intensive manual algorithm selection and hyperparameter tuning, hindering their scalability and reproducibility. To address this point, we present a systematic comparison of four state-of-the-art Automated Machine Learning (AutoML) frameworks—AutoGluon, Auto-Sklearn, FLAML, and TPOT—for automating bio-oil yield prediction. Relying on a dataset of 329 experimental samples from 34 biomass types and seven input features (cellulose, hemicellulose, lignin content, nitrogen flow, heating rate, temperature, and particle size), we demonstrate that FLAML coupled with XGBoost achieves superior predictive performance (, ), thus significantly outperforming both traditional ML models and other AutoML tools. Statistical validation via ANOVA and Tukey’s post-hoc test confirms the robustness of these findings. Our study highlights AutoML’s ability to generate accurate and efficient models for complex pyrolysis systems, substantially reducing reliance on expert knowledge and manual configuration. The developed work establishes AutoML as a scalable solution for optimizing bio-oil production, facilitating more sustainable and data-driven biomass conversion strategies
Modelling methane emissions from ruminant diets with variable forage-to-concentrate ratios and retention times – An in vitro evaluation
No full textThis study aimed to improve the predictive accuracy of in vitro models for estimating in vivo methane (CH4) emissions in Nordic dairy systems by evaluating five forage-to-concentrate (F:C) ratios and incorporating a modelling approach based on ruminal mean retention time (MRT). The tested ratios included 100:0 (100 F), 80:20 (80 F), 60:40 (60 F), 40:60 (40 F), and 20:80 (20 F), where 100 F consisted solely of grass silage, and the remaining diets incorporated barley grain and rapeseed meal as concentrate. All diets were balanced for crude protein (20 % DM), but ether extract and neutral detergent fiber content decreased as concentrate levels increased. To improve the biological relevance of in vitro results, CH4 production was corrected using a ruminal MRT model to better simulate in vivo conditions. Higher concentrate inclusion linearly increased (P < 0.001) total gas and predicted in vivo CH4 production. However, after applying MRT adjustments, the modified model reduced the variation in CH4 predictions across F:C ratios, resulting in values that more closely reflected expected in vivo emissions. The pH declined (P < 0.001) at lower F:C ratios. Organic matter degradability (OMD) followed a quadratic pattern (P < 0.001), peaking in 60 F and 40 F diets and decreasing in 100 F and 20 F. While total volatile fatty acid concentrations were unaffected by F:C ratio, acetate proportion declined linearly (P < 0.001) as concentrate increased, whereas isobutyric and butyric acid proportions rose. Overall, these findings support the application of MRT-adjusted models to enhance the alignment between in vitro predictions and in vivo CH4 emissions
Impatto dell’obesità sugli esiti della tiroidectomia: revisione sistematica della letteratura e meta-analisi
No full textPhoton-counting computed tomography: a revolution in cardiac imaging
No full textPhoton-counting detector computed tomography (PCD-CT) is an emerging advanced CT technology that differs from conventional energy-integrating detector CT (EID-CT) scanners in its ability to directly convert incident X-ray photon energies into electrical signals. Since its commercial market introduction in 2021, several studies have identified advantages of this new technology in the field of cardiovascular imaging, including improved image quality due to an enhanced contrast-to-noise ratio, superior spatial resolution, reduced artefacts, and a reduced radiation dose. Nonetheless, radiation exposure with PCD-CT can vary depending on the acquisition mode and protocol used, highlighting the importance of tailored optimization in clinical practice. In particular, this new technology appears feasible in patients with a high plaque burden independent of morphology, unravelling new phenotypes of plaque, in patients with stents due to the improved visualization of the coronary in-stent lumen, potentially expanding the scope of CT. Early studies and clinical experience support these potential applications of PCD-CT in cardiovascular diagnostics, suggesting workflow optimization and improved patient management. In this review, the authors aim to describe the role of PCD-CT not only in the exclusion of coronary artery disease, grading of coronary stenosis and plaque imaging, but also in evaluation of cardiac chambers and myocardium for tissue characterization trying to understand whether PCD-CT has yet led to a true revolution and significant progress in cardiovascular imaging
Quantitative Metabarcoding Reveals the Effects of Ecological Factors and Invasive Species on Functional Diversity of Freshwater Insects
No full textAquatic insects are the most biodiverse freshwater animals. DNA metabarcoding data are increasingly used to assess variation in insect communities, but they are rarely integrated with information on the abundance or biomass of different taxa that can provide key insights into their functional responses. Here, we combined organismal metabarcoding and biomass estimates of different clades of aquatic insects to assess how different facets of their biodiversity (taxonomic and functional diversity; species traits) are affected by multiple stressors, including invasive species, drought and variation in vegetation. In 44 waterbodies in Northern Italy, we measured environmental features (e.g., pond surface, hydroperiod, and the presence of the invasive crayfish Procambarus clarkii) and collected insect specimens. Specimens were weighed and then used for DNA metabarcoding. The relationship between biomass of seven insect orders and their relative abundance in metabarcoding data was used to obtain quantitative estimates of taxa biomasses across communities and to extract multiple diversity measures. Different facets of biodiversity showed distinct responses to the environmental stressors. The taxonomic diversity of insect communities strongly responded to aquatic vegetation, while functional diversity was more sensitive to the invasive crayfish. Total insect biomass was negatively related to fish presence, but insects with different functional traits showed specific responses to environmental features. Our results show that integrating metabarcoding data with biomass estimates can improve our understanding of community responses to multiple stressors and highlight the importance of considering multiple measures of diversity to get a comprehensive assessment of aquatic insects' responses to environmental variation
Acquired thrombotic thrombocytopenic purpura and HIV infection: a case report and review of the literature
No full textHuman immunodeficiency virus (HIV) is a rare cause of thrombotic microangiopathies (TMA), that can present either with normal ADAMTS13 activity (referred as HIV-related TMA) or suppressed ADAMTS13 activity (referred as HIV-related acquired thrombotic thrombocytopenic purpura, aTTP). The distinct characteristics and management of these two conditions is poorly known, given their rarity and often overlapping features. Here, we report the case of a 46-year-old female patient with TTP who received a diagnosis of HIV infection at her ADAMTS13 relapse and obtained complete remission only with antiretroviral therapy (ART). Moreover, we summarize the existing evidence in the literature about clinical presentation, outcomes and treatment of HIV-related aTTP/TMA