Federico Rossi

Establishing biocrusts on sandy surfaces in harsh desert environments presents significant challenges due to extreme ecological conditions. To address these challenges, this study examined the effects of two organic superabsorbent cellulosic polymers (OSCPs)—carboxymethyl cellulose (CMC) and guar gum (GUA)—on the establishment of cyanobacterial crusts on aeolian sands, specifically using a consortium of Nostoc sp. and Microcoleus vaginatus . The optimal concentrations of GUA and CMC for maximum cyanobacterial growth were determined through preliminary optimization assays. The resulting suspensions, combining OSCPs and cyanobacteria, were applied to sandy substrates. After 60 days of inoculation, the cyanobacterial crusts were analyzed for exopolysaccharide (EPS) accumulation, chlorophyll a content, physical and chemical properties, shear strength, and compressive strength. Additionally, wind tunnel tests were conducted to measure aeolian mass flux under the different treatments. Results indicated that the cyanobacteria + CMC treatment outperformed all others, increasing chlorophyll a by 114% and EPS production by 108% to 280% depending on the EPS fraction. This treatment also enhanced organic carbon and nitrogen accumulation, crust thickness (~21 mm), shear strength (51.39 kPa), and available water capacity (+110%). Furthermore, it raised the wind erosion threshold velocity from 11.5 to 19.7 m s ⁻¹ , reducing aeolian mass flux by over 98% at wind speeds up to 19.7 m s ⁻¹ . In contrast, GUA alone resulted in the lowest compressive strength and mass flux rates. These findings suggest that combining cyanobacteria with OSCPs, particularly CMC, is an effective strategy for promoting biocrust formation and mitigating soil erosion in arid environments.

In automotive engineering, vehicle platooning is a proposed method for improving convoy movements by coordinating them to increase the safety and efficiency of transportation systems. The complexity and stochastic nature of platooning systems provide difficulties for traditional model-checking techniques. However, Statistical Model Checking (SMC) offers a solution by using statistical inference to probabilistically evaluate system features. This paper shows that Uppaal SMC offers a robust framework for assessing platooning systems across various operational scenarios, combining statistical analysis and formal verification methodologies. The behaviour of the platoon is modelled stochastically to cover a wide range of driving scenarios and road surfaces. Using SMC, it has been possible to gauge the safety and functionality of the platoon by estimating the probability of several properties.

Microbial persistence and colonization in the rhizosphere rely on traits that control how cells attach, interact, and organize into biofilms. Among these traits, extracellular polymeric substances (EPS), cell surface hydrophobicity, and adhesion play central roles in the early stages of root surface colonization. Here, we examined 48 isolates of the Pseudomonas fluorescens group associated with Tuber borchii fruiting bodies to explore the relationships among bound and released EPS fractions, hydrophobicity and adhesion ability. All experiments were carried out under controlled in vitro conditions using polystyrene as an abiotic model that mimics the hydrophobic interfaces occurring in soil–root environments. EPS fractions were quantified by Congo red and phenol–sulfuric acid assays, while surface hydrophobicity and adhesion were determined through xylene partitioning and crystal violet staining. The isolates exhibited wide phenotypic variability. Bound EPS showed a strong negative relationship with both hydrophobicity and adhesion, while hydrophobicity was positively associated with adhesion strength. Regression models confirmed that bound EPS and hydrophobicity independently modulate the adhesion response. These findings suggest that thick, hydrated EPS layers can hinder early attachment, while thinner EPS coatings enhance cell–surface interactions. Understanding this functional trade-off provides a basis for the informed selection of microbial inoculants, combining stable biofilm formers with highly adhesive strains to improve persistence and colonization efficiency in rhizosphere environments.

The increasing computational and memory demands of large language models (LLMs) necessitate innovative approaches to optimize resource usage without compromising performance. This paper leverages microscaling floating-point formats, a novel technique designed to address these challenges by reducing the storage and computational overhead associated with numerical representations in LLMs. Unlike traditional floating-point representations that allocate a dedicated scale for each value, microscaling employs a shared scale across a block of values, enabling compact one-byte floating-point representations while maintaining an extended dynamic range. We explore the application of microscaling in the context of 8-bit floating-point formats to significantly reduce memory footprint and computational costs. We tested several configurations of microscaling floats within the GPT-2 LLM architecture, demonstrating that microscaling data formats can achieve competitive accuracy during training and inference, proving its efficacy as a resource-efficient alternative for deploying LLMs at scale. The source code is publicly available at: https://github.com/unipi-dii-compressedarith/llm.c-sve

This paper addresses the verification of neural network robustness against perturbations of input data in vision-based end-to-end autonomous driving systems. The main contributions of this work are: i) We provide a comprehensive analysis of current neural-network-based perception and decision-making components in autonomous vehicles, highlighting their susceptibility to attacks or perturbation. ii) We develop and implement a novel framework for systematically evaluating and verifying the robustness of neural networks against such perturbations, focusing on imperceptible image modifications. iii) We present empirical results demonstrating our verification framework’s effectiveness in identifying weaknesses and providing probabilistic guarantees on network behaviour. Our work underscores the critical need for robust verification methods to ensure the reliability and safety of autonomous driving systems, paving the way for safer integration of neural networks in autonomous vehicles.