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Consensus algorithms are applied in the context of distributed computer systems to improve their fault tolerance. The explosive development of distributed ledger technology following the proposal of ‘Bitcoin’ led to a sharp increase in research activity in this area. Specifically, public and permissionless networks require robust leader selection strategies resistant to Sybil attacks in which malicious attackers present bogus identities to induce byzantine faults. Our goal is to analyse the entire breadth of works in this area systematically, thereby uncovering trends and research directions regarding Sybil attack resistance in today’s blockchain systems to benefit the designs of the future. Through a systematic literature review, we condense an immense set of research records (N = 21,799) to a relevant subset (N = 483). We categorise these mechanisms by their Sybil attack resistance characteristics, leader selection methodology, and incentive scheme. Mechanisms with strong Sybil attack resistance commonly adopt the principles underlying ‘Proof-of-Work’ or ‘Proof-of-Stake’ while mechanisms with limited resistance often use reputation systems or physical world linking. We find that only a few fundamental paradigms exist that can resist Sybil attacks in a permissionless setting but discover numerous innovative mechanisms that can deliver weaker protection in system scenarios with smaller attack surfaces.

Abstract Decentralized cryptocurrency networks, notably those with high energy demand, have faced significant criticism and subsequent regulatory scrutiny. Despite these concerns, policy interventions targeting cryptocurrency operations in the pursuit of sustainability have largely been ineffective. Some were abandoned for fear of jeopardizing innovation, whereas others failed due to the highly globalized nature of blockchain systems. In search of a more effective angle for energy policy measures, this study adopts a consumer-centric perspective, examining the sentiments of Nigerian cryptocurrency users ($n=158$) toward Bitcoin’s sustainability, a representative cryptocurrency known for its high electricity demand. Three main findings emerged: 1) Even among those self-identifying as highly knowledgeable, most considerably underestimated Bitcoin’s electricity consumption. 2) Participants with a more accurate understanding of Bitcoin’s energy demand were more inclined to support sustainability measures. 3) Most of this supportive cohort viewed private entities as the primary stakeholders for implementing such measures. Given these findings, we suggest that consumer education should be at the forefront of policy initiatives aimed at cryptocurrency sustainability. Cryptocurrencies like Bitcoin consume a lot of electricity, raising environmental concerns. This study surveyed 158 Nigerian cryptocurrency users to understand their awareness of the energy use of Bitcoin. The findings revealed that, although many participants considered themselves Bitcoin experts, most underestimated its energy demand. Those who were aware of the actual energy demand were more supportive of measures to reduce it. The study suggests that better educating consumers about the environmental impacts of their cryptocurrency choices, potentially through energy labelling (a practice that provides information on cryptocurrency energy efficiency to users), could lead to more sustainable practices. Graphical Abstract Graphical Abstract

The enormous pressure of the increasing case numbers experienced during the COVID-19 pandemic has given rise to a variety of novel digital systems designed to provide solutions to unprecedented challenges in public health. The field of algorithmic contact tracing, in particular, an area of research that had previously received limited attention, has moved into the spotlight as a crucial factor in containing the pandemic. The use of digital tools to enable more robust and expedited contact tracing and notification, while maintaining privacy and trust in the data generated, is viewed as key to identifying chains of transmission and close contacts, and, consequently, to enabling effective case investigations. Scaling these tools has never been more critical, as global case numbers have exceeded 100 million, as many asymptomatic patients remain undetected, and as COVID-19 variants begin to emerge around the world. In this context, there is increasing attention on blockchain technology as a part of systems for enhanced digital algorithmic contact tracing and reporting. By analyzing the literature that has emerged from this trend, the common characteristics of the designs proposed become apparent. An archetypal system architecture can be derived, taking these characteristics into consideration. However, assessing the utility of this architecture using a recognized evaluation framework shows that the added benefits and features of blockchain technology do not provide significant advantages over conventional centralized systems for algorithmic contact tracing and reporting. From our study, it, therefore, seems that blockchain technology may provide a more significant benefit in other areas of public health beyond contact tracing.

Popular distributed ledger technology (DLT) systems using proof-of-work (PoW) for Sybil attack resistance have extreme energy requirements, drawing stern criticism from academia, businesses, and the media. DLT systems building on alternative consensus mechanisms, foremost proof-of-stake (PoS), aim to address this downside. In this paper, we take a first step towards comparing the energy requirements of such systems to understand whether they achieve this goal equally well. While multiple studies have been undertaken that analyze the energy demands of individual Blockchains, little comparative work has been done. We approach this research question by formalizing a basic consumption model for PoS blockchains. Applying this model to six archetypal blockchains generates three main findings: First, we confirm the concerns around the energy footprint of PoW by showing that Bitcoin's energy consumption exceeds the energy consumption of all PoS-based systems analyzed by at least three orders of magnitude. Second, we illustrate that there are significant differences in energy consumption among the PoSbased systems analyzed, with permissionless systems having an overall larger energy footprint. Third, we point out that the type of hardware that validators use has a considerable impact on whether PoS blockchains' energy consumption is comparable with or considerably larger than that of centralized, non-DLT systems.

Decentralized cryptocurrency networks, notably those with high energy demand, have faced significant criticism and subsequent regulatory scrutiny. Despite these concerns, policy interventions targeting cryptocurrency operations in the pursuit of sustainability have largely been ineffective. Some were abandoned for fear of jeopardizing innovation, whereas others failed due to the highly globalized nature of blockchain systems. In search of a more effective angle for energy policy measures, this study adopts a consumer-centric perspective, examining the sentiments of Nigerian cryptocurrency users (\\(n=158\\)) toward Bitcoin's sustainability, a representative cryptocurrency known for its high electricity demand. Three main findings emerged: 1) Even among those self-identifying as highly knowledgeable, most considerably underestimated Bitcoin's electricity consumption. 2) Participants with a more accurate understanding of Bitcoin's energy demand were more inclined to support sustainability measures. 3) Most of this supportive cohort viewed private entities as the primary stakeholders for implementing such measures. Given these findings, we suggest that consumer education should be at the forefront of policy initiatives aimed at cryptocurrency sustainability.

Ozone depletion events (ODEs) are a common occurrence in the boundary layer during Arctic spring. Ozone is depleted by bromine species, which are most likely emitted from snow, sea ice, or aerosols in an autocatalytic reaction cycle. Previous three-dimensional modeling studies of ODEs assumed an infinite bromine source at the ground. In the present study, an alternative emission scheme is presented in which a finite amount of bromide in the snow is tracked over time. For this purpose, a modified version of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) is used to study ODEs in the Arctic from February to May 2019. The model data are compared to in situ measurements, ozone sonde flights, and satellite data. A simulation of the ODEs in the Arctic spring of 2009 using the infinite-bromide assumption on first-year (FY) ice is transferred to the spring of 2019, which achieves good agreement with the observations; however, there is some disagreement in April 2009 and 2019 with respect to an overestimation concerning both the magnitude and the number of ODEs. New simulations using the finite-bromide assumption greatly improve agreement with in situ observations at Utqiaġvik, Alaska, Zeppelin Mountain, Svalbard, and Pallas, Finland, in April 2019, suggesting that bromide on the sea ice is depleted to an extent that reduces the bromine release. The new simulations also slightly improve the agreement with observations at these sites in February and March. A comparison to measurements near Eureka, Canada, and Station Nord, Greenland, shows that multi-year ice and possibly snow-covered land may be significant bromine sources. However, assuming higher releasable bromide near Eureka does not remove all disagreement with the observations. The numerical results are also compared to tropospheric-BrO vertical column densities generated with a new retrieval method from TROPOspheric Monitoring Instrument (TROPOMI) observations. BrO vertical column densities (VCDs) above 5×1013 molec. cm−2 observed by the satellite agree well with the model results. However, the model also predicts BrO VCDs of around 3×1013 molec. cm−2 throughout the Arctic and patches of BrO VCDs of around 1014 molec. cm−2 not observed by the satellite, especially near Hudson Bay. This suggests that snow at Hudson Bay may be a weaker bromine source in late spring compared to snow in the north.

Resolving the microscopic pairing mechanism and its experimental identification in unconventional superconductors is among the most vexing problems of contemporary condensed matter physics. We show that Raman spectroscopy provides an avenue towards this aim by probing the structure of the pairing interaction at play in an unconventional superconductor. As we study the spectra of the prototypical Fe-based superconductor Ba 1−x K x Fe 2 As 2 for 0.22 ≤  x  ≤ 0.70 in all symmetry channels, Raman spectroscopy allows us to distill the leading s -wave state. In addition, the spectra collected in the B 1 g symmetry channel reveal the existence of two collective modes which are indicative of the presence of two competing, yet sub-dominant, pairing tendencies of d x 2 - y 2 symmetry type. A comprehensive functional Renormalization Group and random-phase approximation study on this compound confirms the presence of the two sub-leading channels, and consistently matches the experimental doping dependence of the related modes. The consistency between the experimental observations and the theoretical modeling suggests that spin fluctuations play a significant role in superconducting pairing. Iron-based superconductors: competing pairing interactions Two collective Raman modes are observed in an iron-based superconductor, indicative of the presence of two competing pairing tendencies alongside the dominant s-wave state. An international team led by R. Hackl from the Walther Meissner Institut perform Raman spectroscopy measurements to probe the structure of pairing interactions in Ba 1− x K x Fe 2 As 2 for 0.22 ≤  x  ≤ 0.70 for all symmetry channels. The Raman spectra not only shows the dominant peak marking the dominant s-wave superconducting pairing state, but also reveals the existence of two collective modes in the B 1 g symmetry channel, indicative of two competing, sub-dominant, paring tendencies of d x 2 - y 2 symmetry type. Numerical calculations confirm the finding and consistently match the doping dependencies of the related modes. The results suggest a significant role of spin-fluctuations in superconducting pairing.

Genome editing outcomes are governed by DNA repair pathways that vary with cell type and state. We developed scOUT-seq (single-cell Outcomes Using Transcript sequencing), a scalable approach that jointly profiles transcriptomes and matched multi-allelic editing outcomes ranging from homology directed repair (HDR) to inter-chromosomal translocations. We mapped editing outcomes in human CD34⁺ hematopoietic stem and progenitor cells (HSPCs), mouse LSK HSPC equivalents, human upper airway organoids, and mouse multi-organ editing. Profiling 500,000 alleles across 74 cell types, scOUT-seq revealed that outcomes in most cell subtypes differ markedly from the bulk average. Various cell types shifted major repair classes, preferred different molecular sequences, and even enriched large structural variants, with distinctive patterns of allelic co-occurrence. Surprisingly, rare stem subtypes diverged from prevalent progenitors, and inhibitory neuron subtypes efficiently incorporated HDR alleles. These data suggest the potential for tailored therapeutic editing that may have been missed by bulk measurements.

Rising atmospheric CO2 levels are predicted to influence forest health directly and indirectly, yet the long-term effects of elevated CO2 (eCO2) on mature trees in natural ecosystems remain poorly understood. Understanding how eCO2 affects susceptibility to biotic stress and alters leaf metabolism is critical for predicting forest responses to climate change. We examined the effects of eCO2 (+150 ppm) on 180-year-old Quercus robur at the Birmingham Institute of Forest Research (BIFoR) Free Air CO2 Enrichment (FACE) facility. From 2016 (pre-treatment) to 2024 (year 8 of enrichment), we monitored natural powdery mildew infection and insect herbivory, alongside targeted and untargeted metabolomic profiling of leaf material collected across the growing season. While seasonal patterns and an overall decline in PM and herbivory were observed, no consistent differences in biotic stress incidence emerged due to eCO2. Metabolomic data revealed subtle but widespread shifts, especially in amino acid, CoenzymeA, and redox pathways. These results suggest that although eCO2 drives extensive metabolic changes, it does not alter biotic stress resistance in mature oaks. Instead, eCO2 appears to promote physiological plasticity that may shape future responses to combined environmental stressors. These insights offer a valuable reference point for interpreting long-term ecosystem dynamics.