Radiation-induced errors in high-energy physics detectors threaten data integrity and uptime, posing significant challenges for reliable operation during the future high-luminosity LHC runs. With over 150,000 ASICs to be deployed in the CMS HGCAL front-end, even a very low per-chip error probability results in a non-negligible failure rate. This work presents a firmware-based, byte-level autorecovery mechanism for the CMS HGCAL Phase-2 front-end electronics, capable of detecting transient upset-related errors and restoring chip operation in real time without requiring a full system reset, ensuring data acquisition continues uninterrupted. The system, implemented in FPGA logic and validated using front-end emulation, executes recovery actions within 4.7µs, corresponding to the measured interval from the data acquisition signal to packet arrival. Overall, the hardware approach is measured to recover approximately 73.5ms faster than software-based intervention. The results demonstrate robust, autonomous operation in radiation-prone environments while preserving continuous data acquisition.
Many reported porous carbons are synthesized through corrosive chemical activating agents and often multistep pyrolysis routes, which generally lead to limited control over the pore structure. In this work, high-performance porous carbons were successfully synthesized from commercial phenolic resin via a chemical activation process using potassium binoxalate (KHC2O4). The influence of activation temperature (750–850 °C) and activator-to-precursor mass ratio (2–4) on the textural properties and CO2 adsorption performance was systematically investigated. The optimal sample exhibited a pore structure characterized by a high specific surface area of 994 m2/g and a substantial narrow micropore volume of 0.49 cm3/g. Surface analysis confirmed the presence of oxygen functional groups, contributing to the surface heterogeneity. Adsorption tests revealed that this series of sorbents achieved superior CO2 uptake capacities of 5.44 mmol/g at 0 °C and 3.81 mmol/g at 25 °C (1 bar). A strong linear correlation was established between the CO2 uptake and narrow micropore volume, identifying ultramicroporosity as the dominant factor governing low-pressure CO2 capture. Furthermore, the adsorbent demonstrated a high CO2/N2 selectivity of 16, rapid adsorption kinetics (90% saturation in ∼4.5 min), and excellent cyclic stability over 5 adsorption–desorption cycles. The isosteric heat of adsorption (34–42 kJ/mol) indicated a physisorption mechanism suitable for energy-efficient regeneration. These results suggest that phenolic resin-derived carbons activated with KHC2O4 are promising candidates for post-combustion CO2 capture.
Infodemics fuel the proliferation of misinformation and disinformation, often outpacing the effectiveness of existing countermeasures. Current interventions—such as debunking and psychological inoculation—typically address either specific instances of false or misleading content or the known manipulative tactics through which such content spreads. While these approaches are effective and scalable, most remain inherently corrective and struggle to anticipate shifts in the disinformation landscape. In this paper, we argue for a theoretical shift in paradigm towards a proactive framework to address disinformation: we propose a content-agnostic model grounded in the identification of recurring linguistic, narrative, logical, and critical thinking patterns that characterize manipulative disinformation prone to virality. By identifying these structural “fingerprints” of disinformation, our framework aims to open new avenues for a research agenda that could inform new inoculation strategies to promote individual and collective resilience. If inoculation provides the methodological foundation for building resistance to manipulation, then identifying structural “fingerprints” provides the core “antigens” that such intervention can target. This, in turn, could enable people to recognize and resist manipulation regardless of the topic, medium, or context through which manipulatory content is delivered. We argue that this approach may offer a sustainable path to building long-term resilience in the face of an evolving and increasingly complex and challenging information ecosystem.
Compartmental models are widely used to analyse epidemiological dynamics, make predictions, and design intervention policies. The COVID-19 epidemic that emerged in December 2019 rapidly led to a global crisis, necessitating the development of various mathematical models to assist policymakers in recommending effective control strategies such as social distancing, contact tracing, and isolation. However, at the early stage of an epidemic, multiple factors may lead to the underestimation or overestimation of the basic reproduction number (R_0) , which plays a key role in determining the future trajectory of the epidemic and informing control strategies. The aim of this study is to analyse the impact of contact tracing and isolation policies on the potential underestimation of R_0 values by employing a compartmental model of Susceptible-Exposed-Presymptomatic-Asymptomatic-Symptomatic-Reported (SEPADR). Theoretical and empirical results show that the implementation of contact tracing and isolation policies at the early stage of the epidemic leads to an underestimation of R_0 . Furthermore, mathematical relations between the exponential growth rate (r) and the basic reproduction number (R_0) are approximately derived in the presence of a contact tracing and isolation policies, thereby generalizing the well-known r-R_0 formulae. The long-term behaviour of the model is assessed by simulating various levels of efficiency in contact tracing and isolation policies.
This study examines the relationship between online social interactions, sentiment dissemination, and stock market returns using Reddit data. We find that a small number of active users significantly influence others by disseminating sentiment within their networks. Active users have a more pronounced influence on less-active users when they share similar beliefs and during periods of increased uncertainty. Moreover, we show that prior-day abnormal network sentiment positively affects future stock market returns. Finally, we evaluate a network-based market timing strategy that effectively reduces drawdowns and highlights the practical implications of online interactions.
