University of Bari Aldo Moro
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Bayesian Multilevel Bivariate Spatial Modelling of Italian School Data
No full textThis paper studies the relationship between the student's abilities in the second year of high school and the infrastructural endowment in all Italian municipalities, using spatial Bayesian modelling. Municipal student scores are obtained by averaging standardized and spatially homogeneous indicators of student outcomes provided by the Invalsi Institute for two subjects, Italian and Mathematics. Given the nature of the data, we employ a multilevel regression model assuming a bivariate Intrinsic Conditionally Autoregressive (ICAR) latent effect to explain the spatial variability and account for the correlation between the two subjects. Bayesian model estimation is obtained by the Integrated Nested Laplace Approximation (INLA), implemented in the \texttt{R-INLA} package. We find that alongside a significant association with the current state of school infrastructure and facilities, spatially structured latent effects are still necessary to explain the different student outcomes across municipalities
Incremental learning and granular computing from evolving data streams: An application to speech-based bipolar disorder diagnosis
No full textWe apply an evolving granular-computing modeling approach, called evolving Optimal Granular System (eOGS), to bipolar mood disorder (BD) diagnosis based on speech data streams. The eOGS online learning algorithm reveals information granules in the flow and design the structure and parameters of a granular rule-based model with a certain degree of interpretability based on acoustic attributes obtained from phone calls made over 7 months to the Psychiatry department of a hospital. A multi-objective programming problem that trades-off information specificity, model compactness, and numerical and granular error indices is presented. Spectral and prosodic attributes are ranked and selected based on a hybrid Pearson-Spearman correlation coefficient. Low attribute-class correlation, ranging from 0.03 to 0.07, is observed, as well as high class overlap, which is typical in the psychiatric field. eOGS models for BD recognition overcome alternative computational-intelligence models, namely, Dynamic Evolving Neural-Fuzzy Inference System (DENFIS) and Fuzzy-set-Based evolving Modeling (FBeM-Gauss), by a small margin in both best and average cases; followed by eXtended Takagi-Sugeno (xTS) and evolving Takagi Sugeno (eTS) types of models. The proposed eOGS model using only 8 of the original acoustic attributes, and about 15 ‘If-Then’ inference rules, has exhibited the best root mean square error, 0.1361, and 91.8% accuracy in sharp BD class estimates. Granules associated to linguistic labels and a granular input-output map offer human understandability with relation to the inherent process of generating class estimates. Linguistically readable eOGS rules may assist physicians in explaining symptoms and making a diagnosis
Cetacean feeding modelling using machine learning: A case study of the Central-Eastern Mediterranean Sea
No full textInvestigating environmental drivers of cetacean feeding behaviour is essential for effective marine resource management, especially in the Mediterranean Sea, a biodiversity hotspot heavily impacted by human activities and climate change. This study realized a pioneer assessment of feeding activity related to the marine envi- ronment for three cetacean species - striped dolphin, common bottlenose dolphin, and Risso’s dolphin - in the Gulf of Taranto (Northern Ionian Sea, Central-eastern Mediterranean) using an innovative Machine Learning (ML) approach. Behavioural data from April 2016 to October 2023, coupled with 20 environmental variables from Copernicus Marine Service and EMODnet-bathymetry datasets, were used to build Cetacean Feeding Models (CFMs) for the target species using Random Forest and RUSBoost algorithms. Multiple subsets of environmental predictors—physiographic, physical, inorganic, and bio-chemical—were employed to develop and evaluate ML models tailored to feeding prediction. Risso’s dolphin resulted to be the best modelled species, with the bio- chemical model based on the RUSBoost algorithm achieving a Balanced Classification Rate (BCR) of 94 %, primarily influenced by 3D chlorophyll-a concentrations, a close proxy for prey availability. The second-best model was the physical one for the common bottlenose dolphin with a BCR of 72 %, influenced by salinity, currents speed, and temperature. These differences in predictive performance might reflect the distinct trophic niches of the studied odontocetes. Finally, simulated predictive maps of Risso’s dolphin feeding habitats for summer months were realized in the Gulf of Taranto, providing actionable insights for conservation and sus- tainable management. The developed CFMs enhance understanding of cetacean feeding preferences and offer a versatile framework for integrating behavioural processes into species distribution models to inform area-based conservation measures, with significant potential for application across other Mediterranean areas
Evaluation of Trends in Influenza A and B Viruses in Wastewater and Human Surveillance Data: Insights from the 2022-2023 Season in Italy
Get PDFWastewater-based epidemiology (WBE) is a recognized, dynamic approach to monitoring the transmission of pathogens in communities through urban wastewater. This study aimed to detect and quantify influenza A and B viruses in Italian wastewater during the 2022–2023 season (October 2022 to April 2023). A total of 298 wastewater samples were collected from 67 wastewater treatment plants (WTPs) across the country. These samples were analyzed for influenza A and B viruses (IAV, IBV) using primers originally developed by the Centers for Disease Control and Prevention (CDC) for real-time PCR and adapted for digital PCR. The overall detection rates of IAV and IBV across the entire study period were 19.1% and 16.8%, respectively. The prevalence of IAV in wastewater showed a gradual increase from October to December 2022, peaking at 61% in December. In contrast, IBV peaked at 36% in February 2023. This temporal discrepancy in peak concentrations suggests different seasonal patterns for the two influenza types. These trends mirrored human surveillance data, which showed influenza A cases peaking at 46% in late December and declining to around 2% by April 2023, and influenza B cases starting to increase significantly in January 2023 and peaking at about 14% in March. IAV concentrations ranged from 9.80 × 102 to 1.94 × 105 g.c./L, while IBV concentrations ranged from 1.07 × 103 to 1.43 × 104 g.c./L. Overall, the environmental data were consistent with the human surveillance trends observed during the study period in the country. These results demonstrate the value of WBE in tracking epidemiological patterns and highlight its potential as a complementary tool to infectious diseases surveillance systems
Impact on ventricular arrhythmic burden of SGLT2 inhibitors in patients with chronic heart failure evaluated with cardiac implantable electronic device monitoring
Get PDFBackground: Sodium-glucose cotransporter 2 (SGLT2) inhibitors have revolutionized the therapeutic scenario of heart failure, demonstrating favorable effects on mortality and quality of life. Previous studies have yielded conflicting data regarding the effects on ventricular arrhythmias. Methods: A prospective observational study was conducted to investigate the anti-arrhythmic properties of SGLT2 inhibitors evaluating the intra-patient difference in major adverse arrhythmic cardiac events (MAACE) over a six-month period in patients with chronic heart failure who were undergoing continuous monitoring using a cardiac implantable electronic device. Results: From January 2022 to January 2023, 82 patients [median age 63 years (IQR 15), male 87 %] were enrolled in the study, with a median follow-up of 28 weeks (IQR 5). The rate of MAACE at baseline was 11 %, without relevant differences in the follow up in terms of major and minor arrhythmic events. In patients with an arrhythmic phenotype at baseline, a mild but non statistically significant reduction of MAACE (from 36 % to 28 %, p = 0.727) was observed and a significant decrease of non-sustained ventricular tachycardia (from 68 % to 32 %, p = 0.022). Conclusions: Our findings suggest potential anti-arrhythmic properties of SGLT2 inhibitors, evident in patients with arrhythmic events before the initiation of the drug
Systemic Risk and Complex Networks in Modern Financial Systems
No full textThe crises of the last years have underlined how much the modern financial systems are today more exposed and vulnerable to systemic risk, defined as the risk of uncontrolled propagation of a crisis of a single player or area of an economic system to a wider system through contagion mechanisms. Systemic risk is more relevant today than in the past due to the increasing interconnection between the players in the economic system and the increasing speed of flows of goods, money and people. All this prompts us to reflect on the need to analyse, predict and manage systemic risk holistically and through the logical-conceptual schemes that can be borrowed from network science
Commento all'art. 182 CCII - Associazione in partecipazione
No full textIl contributo analizza gli effetti della liquidazione giudiziale sui contratti pendenti e, in particolare, sul contratto di associazione in partecipazione nella seguente prospettiva: 1. Le peculiarità del contratto di associazione in partecipazione e il suo scioglimento automatico. – 2. (Segue). L'incompatibilità delle norme che disciplinano il contratto con quelle del CCII. – 3. La definizione delle posizioni di credito e debito delle parti nella liquidazione giudiziale
Systemic Risk and Complex Networks in Modern Financial Systems
No full textThis open access book is a groundbreaking exploration of systemic risk in modern financial systems. Through its theoretical and empirical investigations, it reveals the multidimensionality of systemic risk, the transmission channels of crises, and the interlinkages between physical, transition, and financial risks. It introduces cutting-edge methodologies, including prediction and optimization models based on complex networks, multilayer networks and eXplainable Artificial Intelligence (XAI) approaches, to forecast and measure systemic risk and financial crisis. It provides insight for academics, practitioners, policy and supervisory authorities, and bankers and financial market operators on understanding the links that determine the propagation of financial crises and the emergence of systemic risks. This book is essential for those wishing to better understand systemic risk and its implications
The current status of somatostatin analogs in the treatment of neuroendocrine tumors and future perspectives
No full textIntroduction: Somatostatin analogs (SSAs) were developed as antisecretory agents to palliate hormonal symptoms in patients with functioning neuroendocrine tumors (NETs). Their antiproliferative activity has been established in the phase 3 PROMID and CLARINET trials. SSAs currently represent the standard first-line therapy for the majority of well-differentiated G1/G2 gastroenteropancreatic NETs as well as for pulmonary NETs. Areas covered: An update on the clinical applications of established SSAs for the treatment of NETs is provided. Perspectives on emerging nonpeptide SSAs such as paltusotine and innovative formulations of octreotide (CAM2029) are included. Expert opinion: SSAs represent the cornerstone of treatment for both functioning and nonfunctioning NETs. While standard-dose SSAs have a defined place in the therapeutic algorithm of well-differentiated NETs, uncertainties remain on how to best integrate above-label doses of SSAs in the treatment sequence, particularly when tumor control is the goal. Octreotide and lanreotide appear to be clinically interchangeable, and no signs of superiority of one agent over the other has been observed so far. Whether SSAs may be exploited in the maintenance setting following more aggressive treatments, whether continuing SSAs beyond-progression after first-line therapy could be an effective treatment strategy, and whether new-generation SSAs such as pasireotide could overcome resistance to established SSAs are key areas of investigation
Colonne e pilastri come contenitori epigrafici: un’indagine preliminare nelle chiese della Puglia centro-settentrionale
No full textIndagine preliminare sull'uso di colonne e pilastri in chiese della Puglia centro-settentrionale come contenitori epigrafici, principalmente nel XII secolo