Advanced treatments for PFAS removal from landfill leachate: evaluating biological and ozone based chemical approaches

In the last decades, the rapid industrialization, the population growth and the urbanization led to the increase in waste generation. The practices of reuse, recycling, energy and matter recovery from waste are not sufficient to cope with this rise. Therefore, landfilling still remains a widely used practice. Within landfills, waste undergoes a number of physical, chemical and biological changes and releases micropollutants within the landfill leachate, making this matrix one of the most difficult to treat (Kumar et al., 2023). Leachate is a complex mixture containing very high concentrations of biodegradable and recalcitrant toxic compounds (Qian et al., 2024). It contains a variety of micropollutants, such as polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), pharmaceuticals, personal care products, pesticides, microplastics (MPs), per- and polyfluoroalkyl substances (PFASs), and many more. Although micropollutants are present in very low concentrations they have significant impacts on the ecosystems, economy, human health. The subject of this study are per- and polyfluoroalkyl substances, better known as PFAS, substances synthesized since the 1950s and entered the composition of a great many commercial products because of their outstanding hydro-, oleo-repellency and high stability characteristics. PFASare found in a wide range of products, from fertilizers to food packaging, from personal hygiene products to fire-fighting foams. These, at the end of their life cycle, sent to landfills release fluorinated substances into leachate, where the concentrations can reach up to thousands of μg/L (Gallen et al., 2017). This study stems from the need to identify a solution for PFAS removal in landfill leachate and aims to evaluate the removal efficacy of PFAS and other key chemical parameters of two bench-scale treatment schemes: a biological treatment conducted in a Sequencing Batch Biofilter Granula Reactor (SBBGR) -and an ozone-enhanced biological treatment. Given the matrix complexity and the high chemical stability of PFAS, conventional treatments are inadequate for the removal of fluorinated substances. Hence the need to investigate an integrated approach combining biological degradation with chemical oxidation. Biological treatment was conducted in an SBBGR, an advanced biological treatment system, which is an upflow reactor in which leachate was fed, treated, and extracted sequentially. The plant operated in sequential mode with 8-hour treatment cycles. Each cycle featured a fill, reaction, and discharge phase. The chemical upgrading included an additional phase, the integration of biological degradation with chemical oxidation, performed with ozone at two different doses (4.0 g/L and 5.5 g/L). The discontinuity of the SBBGR system allowed oxidative treatment with ozone to be used in a specific and controlled manner. The ozonation step, following biological treatment, was specific for resistant biological degradation compounds. Ozone, dosed in a controlled manner, allowed for the partial oxidation of recalcitrant substances before returning them back to the biomass action. The experimentation consisted of four phases: a start-up phase, a second phase with a steady state biological , a third phase in which biological treatment was enhanced with ozone at two different dosages, and a final phase in which reactor worked in biological mode fed with leachate at high PFAS concentration. During the preliminary start-up phase, an appropriate feeding program was used: gradual dilutions with water at decreasing ratios to acclimate the biomass to high salinity values (approximately 23 mS/cm) and to stimulate the growth of the species involved in the process. The influent and effluent of all treatment schemes were characterized in terms of traditional parameters and PFAS. Per- and polyfluoroalkyl substances were analyzed by a mass spectrometer interfaced with very high-pressure liquid chromatography

Key performance indexes for the evaluation of geometrical characteristics and subsurface defects through laser line monitoring of laser metal deposition process

Although additive manufacturing (AM) is experiencing a wide diffusion, several limitations persist in the fabrication of metal components, such as low productivity, poor dimensional accuracy, and uncertainty regarding the mechanical properties of the final parts. The main cause of these undesirable effects lies in the intrinsic complexity of the metal AM processes, such as Laser Metal Deposition (LMD). Therefore, accurate monitoring and optimization of process parameters are crucial to ensure the overall quality of the product. Nowadays, various optical methods for monitoring geometrical characteristics are under development. However, insufficient attention has been paid to the potential benefits of using Key Performance Indexes (KPIs) tailored for in-process monitoring of LMD. This paper deals with the evaluation of some KPIs computed utilizing data obtained from a prototype laser line scanner mounted on the deposition head. The system was used to scan AISI 316L monolayer samples produced by the LMD process. Ad-hoc image processing algorithms were employed to process the data, reconstruct the morphology of the component, and extract geometrical information from tracks and layers. Moreover, to assess the occurrence of subsurface defects not directly detectable by the scan, an innovative procedure for creating a geometrical model based on monitoring data was devised. This model derived fundamental KPIs capable of detecting inter-track porosity. Results were then validated through metallographic analyses. The study demonstrated the effectiveness of the proposed procedure in assessing process performance and detecting deposition defects arising from undesired variations in process conditions

Advanced modeling techniques and methodologies for reliable and secure blockchain platforms design

This dissertation explores the transformative potential of Blockchain technology with a primary focus on its application in agri-food traceability and contributions to Software Engineering Education and Training (SEET). Conducted over three years at the Polytechnic University of Bari, this research investigates Blockchain’s capabilities to enhance transparency, security, and efficiency across various domains, with an emphasis on bridging the gap between producers and consumers within supply chains. The work is structured around two main research approaches: a comprehensive analysis of Blockchain technology and the practical development of traceability platforms. The Systematic Literature Review (SLR) conducted as part of this research identifies the primary challenges for Blockchain application in agri-food traceability, including security, architectural design, and the integration of supporting technologies. These insights form the foundation for the proposed traceability models, which reinforce trust between consumers and producers. In addressing Blockchain’s technical challenges, the research delves into quantum-safe cryptography, exploring encryption methods capable of withstanding future quantum computing threats. Additional focus areas include hybrid Blockchain architectures combining public and private models and integrating NoSQL databases to support scalable, flexible platforms. Complementary technologies such as Augmented Reality (AR) and Large Language Models (LLMs) are explored for their potential to extend Blockchain’s usability across various fields, including digital tourism. In the context of SEET, this dissertation examines methods to enhance training. The integration of gamification and the role of LLMs in peer assessment are analyzed as innovative approaches to improve educational outcomes. This focus on workforce training addresses one of the major open challenges identified in the SLR and underscores the importance of a well-prepared workforce to drive future Blockchain innovation. Finally, this dissertation outlines several key areas for future research, including decision-support tools for novice Blockchain developers, the automated generation of smart contracts through LLMs, and the integration of Blockchain in the Internet of Drones (IoD). These avenues represent the potential for expanding Blockchain’s application scope, enhancing its accessibility, and further reinforcing its role as a transformative technology across industries

The effect of Coriolis force on the coherent structures in the wake of a 5MW wind turbine

This study aims to investigate the effect of Coriolis acceleration on the coherent structures in the wake of the NREL-5MW wind turbine, using the Dynamic Mode Decomposition (DMD). The Coriolis acceleration induces an altitude-dependent lateral deviation of the incoming wind direction (veer), which can substantially affect the performance of wind turbines. Large eddy precursor simulations are carried out to generate turbulent inlet velocity profiles. The presence of a veer stretches the turbine wake and influences the dynamics of the coherent structures. We decompose the flow structures to extract a limited subset of relevant flow features that optimally approximate the original data sequence, using the Sparsity-Promoting (SP) version of the DMD algorithm to rank the most relevant modes. It is found that wind veer induces coherent structures with a spanwise velocity component of the same order of the streamwise one and oblique shape. Using proper orthogonal decomposition, we analyze the contribution of the coherent structures to the mean kinetic entrainment. We found that due to wind veer, shorter wavelength mode pairs contribute mostly to wake recovery. Finally, we derive a reduced order model using approximately 70 modes pairs, which reproduces the flow structure with 5%–7% error

On homotopy properties of solutions of some differential inclusions in the W 1, P -topology

We consider a differential inclusion on a manifold, defined by a field of open half-spaces whose boundary in each tangent space is the kernel of a one-form ω. We make the assumption that the corank one distribution associated to the kernel of ω is completely nonholonomic of step 2. We identify a subset of solutions of the differential inclusion, satisfying two endpoints and periodic boundary conditions, which are homotopy equivalent in the W1,p-topology, for any p ∈ [1,+∞), to the based loop space and the free loop space respectively

Angiolo Mazzoni in Colombia: un patrimonio di immagini: architettura di immagini - immagini di architettura

La storia, personale e professionale, dell’architetto Angiolo Mazzoni, noto per i suoi edifici pubblici ferroviari e postali disseminati in tutto il territorio italiano, dimentica spesso di raccontare il quindicennio da lui trascorso in Colombia, non attribuendo così il giusto valore ad una opera che, seppur perlopiù non realizzata, contribuisce a ricostruire l’immagine poliedrica di una figura di spessore del Novecento come quella del Mazzoni. La ricerca ha quindi voluto far emergere il portato dell’opera colombiana dell’architetto italiano, restituendo il giusto valore all’architettura rappresentata, ovvero riconoscendo al cospicuo corpus grafico lascito dell’architetto il valore di disegno teorico. Il periodo colombiano ha, di fatto, rappresentato per il Mazzoni la sua personale “stagione dell’architettura di carta”, restituendoci probabilmente le sue più floride riflessioni, nonché, secondo una personale visione e lettura, le sue più personali interpretazioni del fatto architettonico. L’indagine ha voluto sviluppare una attività di tipo interpretativa favorita dal disegno e dal rilievo, al fine di rendere, attraverso i mezzi più opportuni, non una mera restituzione pratica dell’architettura, quanto piuttosto la sua essenza spaziale anche nel suo statuto di disegno. L’analisi del materiale conservato tra Italia e Colombia ha, allora, permesso di ricostruire una geografia dei progetti colombiani, restituendo altresì l’estrema capacità dell’architetto di confrontarsi con i temi architettonici più disparati, a differenza di quanto si è abituati in relazione all’opera italiana. Scrivere il racconto dei progetti e, più in generale, dell’esperienza colombiana è significato scrivere un racconto fatto di rappresentazioni grafiche, di immagini: i disegni. Quello che ne è risultato è stata una narrazione visuale che attraverso il riordino, la catalogazione e la selezione dei disegni autografi ha permesso di raccontare il quindicennio sudamericano, palesando al contempo l’assenza di architettura intesa quale fatto tangibile. Lo studio condotto ha privilegiato l’indagine dello spazio, nella sua declinazione di spazio disegnato, intendendo il disegno quale tessuto di idee, ma anche quale luogo di sperimentalismo; un disegno teorico disvelatore dell’interiorità del suo autore, ma anche di una realtà autonoma, propria e caratteristica del disegno di architettura. In questo senso nel ricco panorama di elaborati grafici redatti da Mazzoni si è letta una significatività rispetto al valore del disegno: il vasto patrimonio visuale è riuscito a restituire una architettura dell’immagine in grado di rendersi altresì palinsesto infinito di forme che attingono dalla memoria, nonché sistema disvelatore di un particolare modo di vedere e far vedere il fatto architettonico, seppur rimasto su carta

Deep learning strategies for semantic segmentation of pediatric brain tumors in multiparametric MRI

Automated segmentation of pediatric brain tumors (PBTs) can support precise diagnosis and treatment monitoring, but it is still poorly investigated in literature. This study proposes two different Deep Learning approaches for semantic segmentation of tumor regions in PBTs from MRI scans. Two pipelines were developed for segmenting enhanced tumor (ET), tumor core (TC), and whole tumor (WT) in pediatric gliomas from the BraTS-PEDs 2024 dataset. First, a pre-trained SegResNet model was retrained with a transfer learning approach and tested on the pediatric cohort. Then, two novel multi-encoder architectures leveraging the attention mechanism were designed and trained from scratch. To enhance the performance on ET regions, an ensemble paradigm and post-processing techniques were implemented. Overall, the 3-encoder model achieved the best performance in terms of Dice Score on TC and WT when trained with Dice Loss and on ET when trained with Generalized Dice Focal Loss. SegResNet showed higher recall on TC and WT, and higher precision on ET. After post-processing, we reached Dice Scores of 0.843, 0.869, 0.757 with the pre-trained model and 0.852, 0.876, 0.764 with the ensemble model for TC, WT and ET, respectively. Both strategies yielded state-of-the-art performances, although the ensemble demonstrated significantly superior results. Segmentation of the ET region was improved after post-processing, which increased test metrics while maintaining the integrity of the data