Archivio Istituzionale della Ricerca - Università degli Studi di Pavia
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An experimental study of the mineral carbonation potential of the Jizan Group basalts
No full textThe ability of Jizan basalts, a potential subsurface mineral carbon storage formation located in southwest Saudi Arabia, to carbonate water-dissolved CO2 has been examined through a set of closed system batch fluid-rock experiments performed at 60 °C. Two Jizan basalt samples were collected from a CO2 injection pilot test well at depths of 375 and 665 m below surface. The basalt samples are dominated by intermediate plagioclase (An∼60) and a Ca-Mg pyroxene with minor chlorite and zeolite. The cleaned basalts were placed into individual sealed reactors along with either aqueous sodium carbonate or sodium bicarbonate solutions, and the experiments were conducted over a period of up to 250 days. The reactive fluid compositions in all experiments suggest that the dissolution of plagioclase dominates the basalt dissolution; calculations suggest that the fluids rapidly approach pyroxene equilibrium. The reactive fluids rapidly become saturated with respect to calcite. SEM imaging and EDS analysis confirm calcite growth on the basalt grains. In contrast, Al-bearing secondary minerals were not identified despite apparently being retained by the solid phases during the experiments. Notably, the dissolution rates of the Jizan basalts slowed considerably over time during the static batch experiments. This observation suggests that the relatively rapid dissolution of basalt by acidic CO2-rich fluids in a dynamic flow system creating a dissolution zone and precipitation zone, is essential for efficient in situ mineralization of CO2 in basalts
Dietary Inflammatory Score (DIS)'s and Lifestyle Inflammatory Score (LIS)'s Impact on Multiple Sclerosis Severity
No full textBackground/Objectives: Multiple sclerosis (MS) is an immune-mediated demyelinating disease of the central nervous system with variable degrees of inflammation and gliosis. Diet and lifestyle factors could influence MS development and also contribute to inflammation. The current study aims to evaluate the relationship between dietary and lifestyle inflammatory potential and multiple sclerosis severity. Methods: A cross-sectional study design was employed. Data collection included demographic, neurological, and nutritional information. The Dietary Inflammatory Score (DIS) and Lifestyle Inflammatory Score (LIS) were calculated based on the reference protocol. Results: One hundred and seven participants (69.2% female; mean age, 50.6 ± 11.6 years) completed the study. The anti-inflammatory LIS group had significantly higher proportions of normal-weight (p = 0.000) and physically active (p = 0.022) participants. A greater proportion of women exhibited an anti-inflammatory lifestyle compared to men (80% vs. 20%; p = 0.023). No relation was retrieved between the DIS, LIS, and MS Severity Score (MSSS). When analyzing the single DIS components, leafy greens were associated with MS severity (OR 1.67; 95% CI, 1.50–18.74; p = 0.009). Among the LIS components, “high physical activity” (OR 5.51; 95% CI, 1.66–18.30; p = 0.005) and “heavy drinking” (OR 5.61; 95% CI, 1.19–26.47; p = 0.029) were related to lower MS severity. Conclusions: Although no differences were found in the total Dietary and Lifestyle Inflammatory Scores, some of their components might be connected with MS severity. Further intervention studies are needed to validate these findings
Protocol to quantify bacterial burden in time-kill assays using colony-forming units and most probable number readouts for Mycobacterium tuberculosis.
No full textHere, we present a protocol to perform a time-kill assay (TKA) to quantify bacterial burden at multiple time points using colony-forming units and most probable number readouts simultaneously. We describe steps for preparing inoculum, experimental conditions, and sampling bacterial counts. We then detail procedures for quantification and analysis. TKAs provide longitudinal data reflecting the dynamics of the antibiotic effect over time against a planktonic culture and quantify the concentration-effect relationship. For complete details on the use and execution of this protocol, please refer to Van Wijk et al
Wastewater-based epidemiology of influenza viruses: a systematic review
No full textIntroduction
Wastewater-based epidemiology (WBE) has emerged as a valuable public health tool for monitoring the circulation of many pathogens, including influenza viruses (IVs). The general aim of this study is to systematically retrieve and summarize evidence on the use of WBE for supporting influenza surveillance. Specific objectives are: (i) to map influenza monitoring activities using WBE; (ii) to assess the performance of viral recovery methods; (iii) to explore association with clinical data; (iv) to evaluate the feasibility of typing/subtyping IVs directly from wastewater.
Methods
We conducted a systematic review following the PRISMA guidelines, focusing on original data from peer-reviewed studies identified through PubMed/Medline, Scopus, and Web of Science.
Results
Of 882 identified citations, 42 studies were included in the review. IVs detection was reported in all but one study, although typically at lower concentration than SARS-CoV-2. Thirteen studies (38.09 %) performed comparative analysis of different protocols, with mostly inconclusive results. Detection of IVs in the solid fraction of wastewater samples generally outperformed detection in the supernatant/liquid. Additionally, we describe the findings from 22 studies (52.38 %) that examined the link between environmental viral concentrations and clinical data, and 14 studies (33.33 %) that described IVs subtyping in wastewater.
Conclusion
WBE has the potential to monitor influenza circulation in humans and animals, offering insights into outbreak size and circulating IVs subtypes. However, several key areas remain unexplored. Further research is needed to refine experimental techniques and standardize protocols, and to understand how to successfully integrate WBE data into public health strategies for influenza control
Diritto e religione in Italia. Dai concordati alla problematica islamica
No full textIl volume costituisce la terza edizione aggiornata del manuale di diritto ecclesiastico di Luciano Musselli
A molecular sieve boosts perovskite stability
No full textHighly efficient and stable perovskite solar cells are fabricated by introducing a molecular sieve which finely controls the 2D/3D heterointerface reactions
Sicurezza basata sull'AI per l'Internet of Things e Federated Learning
No full textQuesta tesi indaga le sfide chiave nel miglioramento della sicurezza, della privacy e della conformità dei sistemi dell'Internet of Things (IoT), concentrandosi sulle vulnerabilità e proponendo soluzioni per operazioni che preservano la privacy in diversi scenari. Con la crescita dei sistemi IoT, con innumerevoli dispositivi interconnessi che generano vasti flussi di dati, aumentano anche le probabilità di anomalie e attacchi malevoli. Per affrontare queste sfide, la tesi è composta da tre parti.
Nella Part A, l’attenzione è focalizzata sull'assicurare che i dispositivi IoT rispettino gli Accordi sui Livelli di Servizio per la Sicurezza e la Privacy (SLA) durante l'acquisizione di servizi, verificando al contempo l'affidabilità dei dispositivi attraverso l'individuazione avanzata delle anomalie. Viene sviluppata una soluzione basata sul Deep Reinforcement Learning (DRL) per insegnare ai dispositivi IoT a conformarsi autonomamente agli SLA, garantendo privacy e sicurezza in modo più autonomo.
Modelli di rilevamento delle anomalie guidati dall'AI e la tecnologia blockchain sono impiegati per verificare l'affidabilità dei dispositivi, consentendo ai sistemi IoT di mantenere la loro "liveness" rilevando e rispondendo a comportamenti sospetti. Ciò garantisce che i dispositivi che forniscono servizi siano affidabili e funzionino correttamente. Viene inoltre proposta un'approccio di Federated Learning (FL) che preserva la privacy per il rilevamento collaborativo delle anomalie nei dispositivi IoT, sfruttando la blockchain per coordinare il processo di apprendimento senza condivisione centralizzata dei dati.
Integrando la conformità agli SLA e il rilevamento delle anomalie, il framework consente ai dispositivi IoT di proteggere autonomamente i servizi e verificare l'affidabilità dei dispositivi collegati, mantenendo sia la sicurezza che l'efficienza operativa.
Inoltre, nella Part B, dato il ruolo centrale del FL nella soluzione proposta per il rilevamento delle anomalie, questa tesi affronta le principali sfide di sicurezza non risolte in questo campo, concentrandosi in particolare su avvelenamento del modello, attacchi backdoor e attacchi di inferenza.
L’avvelenamento del modello coinvolge partecipanti malevoli che corrompono il modello globale, mentre gli attacchi backdoor introducono comportamenti nascosti attivati in condizioni specifiche. Gli attacchi di inferenza, invece, mirano a estrarre informazioni private dai modelli, compromettendo così la privacy dei dati. Questa parte della tesi analizza sistematicamente queste minacce in vari approcci di FL, tra cui Federated Learning Orizzontale, Verticale e Federated Transfer Learning, identificando vulnerabilità e proponendo contromisure per mitigarle, con l'obiettivo di migliorare la sicurezza e la privacy negli ambienti di apprendimento decentralizzati.
Nelle considerazioni finali, la tesi esamina, nella Part C, l'importanza della Cyber Threat Intelligence (CTI) nella cybersicurezza, soprattutto alla luce dell'enorme quantità di dati generati dalle interazioni umane con i dispositivi IoT. Poiché informazioni preziose riguardanti minacce emergenti potrebbero essere nascoste in questi dati, vengono utilizzate tecniche avanzate di Natural Language Processing (NLP) per analizzare informazioni testuali derivate dalle interazioni con assistenti basati su modelli di linguaggio naturale. Questa sezione sottolinea il ruolo critico del CTI nei sistemi distribuiti e la necessità di ulteriori ricerche in questo ambito.
Nel complesso, questa ricerca contribuisce con significativi avanzamenti nella sicurezza dell’IoT, nel Federated Learning, e fornisce alcune riflessioni sull'importanza della CTI, proponendo soluzioni orientate alla privacy per le sfide contemporanee. L’obiettivo è rendere i sistemi IoT più autonomi e intelligenti, mantenendo solidi meccanismi di sicurezza e privacy durante i processi di rilevamento delle anomalie e di acquisizione dei servizi.This thesis investigates key challenges in enhancing the security, privacy, and compliance of Internet of Things (IoT) systems, focusing on vulnerabilities and proposing solutions for privacy-preserving operations across various scenarios. As IoT systems grow with countless interconnected devices generating vast streams of data, they become increasingly prone to anomalies and malicious attacks.
To address these challenges, this thesis is composed of three parts.
In Part A, the focus is on ensuring that IoT devices adhere to Security and Privacy Service Level Agreements (SLAs) during service acquisition, while also verifying the reliability of devices through advanced anomaly detection. A Deep Reinforcement Learning (DRL)-based solution is developed to teach IoT devices to autonomously comply with SLAs, ensuring privacy and security in a more autonomous manner.
AI-driven anomaly detection models and blockchain technology are employed to verify device reliability, allowing IoT systems to maintain their "liveness" by detecting and responding to suspicious behavior. This ensures that devices providing services are trustworthy and functioning correctly. A privacy-preserving Federated Learning (FL) approach is also proposed for collaborative anomaly detection across IoT devices, leveraging blockchain to coordinate the learning process without centralized data sharing.
By integrating SLA compliance and anomaly detection, the framework enables IoT devices to autonomously secure services and verify the reliability of connected devices, maintaining both security and operational efficiency.
Moreover, in Part B, given the central role of FL in the proposed solution for Anomaly Detection, this thesis addresses key unresolved security challenges in the field, specifically focusing on model poisoning, backdoor attacks, and inference attacks.
Model poisoning involves malicious participants corrupting the global model, while backdoor attacks introduce hidden behaviors activated under specific conditions. Inference attacks, instead, aim to extract private information from models, thus compromising data privacy. This part of the thesis systematically analyzes these threats across Horizontal FL, Vertical FL, and Federated Transfer Learning approaches, identifying vulnerabilities and proposing countermeasures to mitigate them, with the goal of improving security and privacy in decentralized learning environments.
In its final remarks, this thesis examines, in Part C the significance of Cyber Threat Intelligence (CTI) in cybersecurity, particularly in light of the immense data generated through human interactions with IoT devices. Given that valuable insights regarding emerging threats may be concealed within this data, advanced Natural Language Processing (NLP) techniques are employed to analyze textual information derived from interactions with natural language model-based assistants. This section underscores the critical role of CTI in distributed systems and emphasizes the need for future research in this area.
Overall, this research contributes significant advancements in IoT security, Federated Learning, and gives some insights on the importance of CTI, proposing novel, privacy-preserving solutions to contemporary challenges. It aims to make IoT systems more autonomous and intelligent while maintaining robust security and privacy mechanisms throughout the anomaly detection and service acquisition processes
Clinical Heterogeneity of Early-Onset Autoimmune Gastritis: From the Evidence to a Pediatric Tailored Algorithm
No full textBarriers and facilitators to starting and staying on Ketogenic Diet Therapy for children with epilepsy: a scoping review
No full textBackground: Ketogenic diet therapy (KDT) is a group of high-fat, low-carbohydrate, moderate protein diets that are an effective treatment option for children and young people with drug-resistant epilepsy. However, 45% of patients referred to start KDT, who are medically eligible to do so, do not start diet. A further 25% discontinue KDT before 3 months.
Aim: to explore barriers and facilitators for children and young people with epilepsy to start or continue on KDT, and their families. Based on the Participants, Concept and Context (PCC) framework, this review included children and young people with drug-resistant epilepsy (P) referred for KDT (C) or their families, who had expressed their views regarding barriers or facilitators for starting or continuing on dietary treatment (C).
Methods: This scoping review followed JBI methodology. Identified barriers/facilitators were matched to relevant sections of the 'Capability, Opportunity, Motivation - Behaviour' (COM-B) model and organised according to the phases of the patient journey: pre-diet, diet initiation and maintenance.
Results: 60 studies were included. 15 barriers and 9 facilitators were identified for the pre-diet and initiation stages; 19 barriers and 14 facilitators were identified for staying on KDT once the diet had been fully established.
Conclusions: Barriers and facilitators for children and young people who are considering starting KDT, or who are currently following KDT, and their families, are multifactorial and extend beyond the level of the individual. Our findings will help identify areas to prioritise for interventions to support patients and their families