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Low‐Content Ru Catalysts for Efficient CO2 Methanation
Get PDFA series of low-content Ru-based catalysts supported on Al2O3, TiO2, CeO2, MgO, and ZrO2 were synthesized via incipient wetness impregnation and evaluated for CO2 methanation. Among them, Ru/TiO2 exhibited the highest activity in terms of turnover frequency (TOF = 3.35 s−1) and selectivity toward CH4 (> 95%). To elucidate the underlying reaction mechanism, operando Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS) analyses were performed, revealing the presence of key surface intermediates. Based on these observations, several power-law and Langmuir–Hinshelwood–Hougen–Watson (LHHW) kinetic models were formulated and fitted to experimental data. A detailed comparison of different mechanistic hypotheses was conducted, highlighting the role of CO2 dissociation and stepwise hydrogenation pathways. A two active site model that considers CO2 adsorption on the basic sites of the support and the continuation of the reaction by hydrogen spillover from Ru nanoparticles provided the best agreement with experimental results. Overall, the combination of catalytic testing, operando spectroscopy, and kinetic modeling offers a comprehensive understanding of the CO2 methanation pathway over Ru/TiO2 and provides a reliable basis for catalyst comparison and reactor design to effectively manage thermal issues
Detecting TLS Protocol Anomalies Through Network Monitoring and Compliance Tools
Get PDFThe Transport Layer Security (TLS) protocol is widely used nowadays to create secure communications over TCP/IP networks. Its purpose is to ensure confidentiality, authentication, and data integrity for messages exchanged between two endpoints. In order to facilitate its integration into widely used applications, the protocol is typically implemented through libraries, such as OpenSSL, BoringSSL, LibreSSL, WolfSSL, NSS, or mbedTLS. These libraries encompass functions that execute the specialized TLS handshake required for channel establishment, as well as the construction and processing of TLS records, and the procedures for closing the secure channel. However, these software libraries may contain vulnerabilities or errors that could potentially jeopardize the security of the TLS channel. To identify flaws or deviations from established standards within the implemented TLS code, a specialized tool known as TLS-Anvil can be utilized. This tool also verifies the compliance of TLS libraries with the specifications outlined in the Request for Comments documents published by the IETF. TLS-Anvil conducts numerous tests with a client/server configuration utilizing a specified TLS library and subsequently generates a report that details the number of successful tests. In this work, we exploit the results obtained from a selected subset of TLS-Anvil tests to generate rules used for anomaly detection in Suricata, a well-known signature-based Intrusion Detection System. During the tests, TLS-Anvil generates .pcap capture files that report all the messages exchanged. Such files can be subsequently analyzed with Wireshark, allowing for a detailed examination of the messages exchanged during the tests and a thorough understanding of their structure on a byte-by-byte basis. Through the analysis of the TLS handshake messages produced during testing, we develop customized Suricata rules aimed at detecting TLS anomalies that result from flawed implementations within the intercepted traffic. Furthermore, we describe the specific test environment established for the purpose of deriving and validating certain Suricata rules intended to identify anomalies in nodes utilizing a version of the OpenSSL library that does not conform to the TLS specification. The rules that delineate TLS deviations or potential attacks may subsequently be integrated into a threat detection platform supporting Suricata. This integration will enhance the capability to identify TLS anomalies arising from code that fails to adhere to the established specifications
Optical coherence photoacoustic microscopy for 3D cancer model imaging with AI-assisted organoid analysis
Get PDFCancer organoids and cancer spheroids are 3D cell culture models with distinct yet overlapping purposes in cancer research. Various commercially available optical imaging techniques have been employed to study these cell cultures, but these methods suffer from various limitations such as the requirement of fluorescence labeling, complicated sample handling, and limited image volume size. In this work, we demonstrate a multimodal optical coherence photoacoustic microscopy (OC-PAM) system for the study of these models, overcoming these limitations. We first performed a longitudinal study using optical coherence microscopy (OCM) for breast cancer organoids. Using the OCM imaging results, artificial intelligence (AI)-based algorithms were developed to automatically segment individual organoids and classify their viability over time using a radiomics texture feature approach, enabling robust, quantitative tracking and classification at the single-organoid level. To supplement OCM’s contrast, we then performed OC-PAM imaging of spheroid models with both melanin positive and melanin negative cells. In the second study, the OC-PAM images clearly mapped the distribution of melanin positive cells hidden amongst melanin negative cells. These results suggest that OC-PAM coupled with AI techniques can be a powerful tool to study cancer organoids and cancer spheroids
The Impact of New Energy Carriers in Road Tunnels, Part I: Focus on Battery Electric Vehicles
No full textTransport safety is a critical consideration in the design and operation of infrastructure, particularly in road tunnels, where specific measures are necessary to protect both users and first responders. Traditional safety concepts have been developed primarily for conventional vehicles, addressing scenarios such as tunnel fires, in which toxic gas emissions, reduced visibility, and thermal loads pose significant hazards. In response to the global climate crisis, there is a growing transition towards alternative fuels and vehicle technologies, including hydrogen, natural gas, and battery electric vehicles (BEVs). These technologies introduce new storage systems and operational characteristics, which differ from those of diesel and petrol vehicles and may affect established safety standards. A substantial body of analytical, numerical, and experimental research has investigated the behaviour of alternative fuel vehicles in fire and accident scenarios. However, the diversity of studies and data makes it challenging to synthesise the findings into a comprehensive assessment. This paper provides a structured review of the literature, focusing on BEVs, their battery technologies, thermal runaway mechanisms, toxic releases, and incident scenarios, as well as associated fire suppression strategies. Hybrid vehicles are also considered for comparative purposes. The consequences of these hazards for life safety are examined, including evacuation procedures, preventive measures, and qualitative risk assessment. By integrating current knowledge, the study aims to evaluate the adequacy of tunnel safety measures and identify areas requiring further research, providing a coherent framework for understanding the risks associated with new energy carriers in road transport
Gli Autoencoder nel Digital Image Processing
Get PDFQuesto studio esplora l'efficacia degli autoencoder nell'apprendimento non supervisionato applicato al Digital Image Processing, con un focus innovativo sulla generazione di modelli archetipici definiti "pseudoimmagini". Partendo dall'architettura fondamentale encoder-decoder, il report analizza come lo spazio latente possa essere utilizzato non solo per il denoising standard, ma per formalizzare pattern strutturali profondi. La metodologia, originariamente sviluppata per la spettroscopia Raman, viene qui traslata all'analisi di immagini satellitari degli Stati Uniti, sfruttando la regolarità geometrica del Public Land Survey System (PLSS). I risultati dimostrano che l'autoencoder, attraverso il clustering dello spazio latente, è in grado di identificare e ricostruire visivamente i centroidi dei cluster (pseudoimmagini), fornendo rappresentazioni idealizzate di paesaggi rurali, industriali e di transizione. Il lavoro sottolinea la versatilità transdisciplinare del concetto di ''pseudospettro'' come strumento per la creazione di librerie di riferimento sintetiche e la validazione della logica interna dell'Intelligenza Artificiale
Engineering MgFe2O4 nanoparticles to enhance magnetic, optical, and dielectric performance
Get PDFThis research investigated the effects of solution pH during synthesis and annealing on the structural, magnetic, optical, and dielectric properties of MgFe2O4 synthesised using the citric acid-assisted sol-gel auto-combustion method. Structural data showed that crystallinity, lattice parameters, and cation distribution were strongly influenced by both pH and annealing temperature. Optimal morphology and crystallinity were achieved at pH 7 and an annealing temperature of 550 °C for 3 h, under which cation ordering and defect reduction were effectively achieved. Magnetic characterisation revealed a significant improvement in saturation magnetisation (20.6 emu/g) under optimal conditions, attributed to increased super-exchange interactions. The optical bandgap was modulated by more than 11% (2.04 to 2.30 eV) by adjusting pH-mediated surface chemistry and subsequent defect states. The sample synthesised at pH 9 exhibited the highest values of ε' , ε" , and σac . These results conclusively demonstrate that accurate control of synthesis pH and annealing enables the design of multifunctional properties in MgFe2O4 nanoparticles, providing a solid foundation for their application in high-end magnetic, optical, and electronic devices
Enhancing the Environmental, Social, and Economic Sustainability of Infrastructures: A Framework for Pavement Management
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Group factorisation for smaller signatures from cryptographic group actions
Get PDFCryptographic group actions have gained significant attention in recent years for their application on post-quantum Sigma protocols and digital signatures. In NIST’s recent additional
call for post-quantum signatures, three relevant proposals are based on group actions: LESS,
MEDS, and ALTEQ. This work explores signature optimisations leveraging a group’s factorisation. We show that if the group admits a factorisation as a semidirect product of subgroups,
the group action can be restricted on a quotient space under the equivalence relation induced
by the factorisation. If the relation is efficiently decidable, we show that it is possible to
construct an equivalent Sigma protocol for a relationship that depends only on one of the
subgroups. Moreover, if a special class of representative of the quotient space is efficiently
computable via a canonical form, the restricted action is effective and does not incur in
security loss. Finally, we apply these techniques to the group actions underlying LESS and
MEDS, showing how they will affect the length of signatures and public keys
Improving state-of-the-art vertex sorting algorithms to compute the maximum common induced subgraph
Get PDFThe Maximum Common Induced Subgraph problem is a longstanding challenge in graph theory and combinatorial optimization, recognized for being NP-complete and its applications across chemistry, network analysis, and pattern recognition. State-of-the-art methods, such as the McSplit algorithm and its successors, employ a recursive branch-and-bound procedure to navigate the vast solution space. The efficiency of this search is critically dependent on the initial vertex sorting heuristic, which not only guides the algorithm toward a good solution but also structures the search tree for the computationally intensive proof of optimality. The original algorithm relies on a simple node degree heuristic, which is often suboptimal. This paper systematically investigates the influence of alternative vertex-ordering heuristics on McSplitDAL, a state-of-the-art variant of McSplit. We integrate five node-ranking heuristics (namely, PageRank, Betweenness Centrality, Closeness Centrality, Local Clustering Coefficient, and a modified Katz Centrality) into the McSplitDAL framework. We analyze their effect on search-space exploration, pruning efficiency, convergence behavior, and execution speed. We also investigate how they shape the algorithmic search and affect the solver’s ability to approach or prove optimality under constrained computational budgets. Experimental results across heterogeneous datasets reveal that specific heuristics, such as PageRank and Katz Centrality, consistently promote more effective pruning and higher-quality intermediate solutions, offering valuable insights into the relationship between graph topology-derived measures and branch-and-bound performance
A deeper understanding of flooding dynamics in gas diffusion electrodes for CO2 electrolyzer: how interfacial pressure shapes gas–liquid stability
Get PDFGas-fed CO2 electrolyzers are a promising technology for sustainable fuel and chemical production, but their industrial deployment is limited by the instability of gas diffusion electrodes (GDEs), particularly in microfluidic flow cells (MFCs). A key failure mechanism is electrode flooding, which discontinues CO2 transport and favours hydrogen evolution. Although pressure control across the gas–liquid interface has emerged as a strategy to mitigate flooding, the precise role of differential pressure (ΔP) between gas and liquid side of the GDE remains poorly understood and inconsistently defined in literature. In this work, we systematically explore how gas and liquid pressure management alters the GDE interface, focusing on the understudied “flow-by” regime. Using Cu nanoparticles as a model catalyst and operating at industrially relevant current densities (0.5 A cm−2), we monitor flooding dynamics through real-time pressure readings, product selectivity analysis, electrochemical impedance spectroscopy (EIS) and field emission scanning electron microscopy (FE-SEM) in different electrochemical setups with several commercial Gas Diffusion Layers (GDL). Our results demonstrate that a ΔP of 30 mbar can fully suppress flooding, preserving catalyst performance and enabling selective CO2 reduction for over 6 h at 0.5 A cm−2, almost an order-of-magnitude improvement over uncontrolled system. The experimental ΔP value is confirmed by multiphysics simulations, by modelling capillary-driven liquid invasion and gas transport, in which a predicted onset value of 20 mbar is defined as the required value to prevent the flooding. This work provides the first integrated framework combining pressure tuning, diagnostics, and multi-physics simulation to define and optimize flow-by operation, offering actionable insights for designing robust, high-performance CO2 electrolyzers