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Integrated information theory (IIT) assesses the degree of consciousness in living organisms from an information-theoretic perspective. This theory can be generalised to other systems, including those exhibiting criticality. In this study, we applied IIT to the collective behaviour of Plecoglossus altivelis and observed that the group integrity (Φ) was maximised at the critical state. Multiple levels of criticality were identified within the group, existing as distinct subgroups. Moreover, these fragmented critical subgroups coexisted alongside the overall criticality of the group. The distribution of high-criticality subgroups was heterogeneous across both time and space. Notably, core fish in the high-criticality subgroups were less affected by internal and external stimuli compared to those in low-criticality subgroups. These findings are consistent with previous interpretations of critical phenomena and offer a new perspective on the dynamics of an empirical critical state.

With the increasing number of services and industries including nuclear, chemical, aerospace, and automotive sectors in cyber‐physical systems (CPSs), systems are being severely overloaded. CPSs comprises mixed‐critical tasks which are of either safety‐critical (high) or non‐safety critical (low). In traditional task scheduling, most of the existing scheduling algorithms provide poor performance for high‐criticality tasks when the system experiences overload and do not show explicit separation among different criticality tasks to take advantage of using cloud resources. Here, we propose a framework to schedule the mixed‐criticality tasks by analyzing their deadlines and execution times which leverage the performance of parallel processing through OpenMP. The proposed framework introduces a machine learning‐based prediction for a task offloading in the cloud. Moreover, it illustrates to execute a selected number of low‐criticality tasks in the cloud while the high‐criticality tasks are run on the local processors during the system overload. As a result, the high‐criticality tasks meet all their deadlines and the system achieves a significant improvement in the overall execution time and better throughput. In addition, the experimental results employing OpenMP show the effectiveness of using the partitioned scheduling over the global scheduling method upon multiprocessor systems to achieve the tasks isolation.

In this work we study the stability of the equilibria reached by ecosystems formed by a large number of species. The model we focus on are Lotka-Volterra equations with symmetric random interactions. Our theoretical analysis, confirmed by our numerical studies, shows that for strong and heterogeneous interactions the system displays multiple equilibria which are all marginally stable. This property allows us to obtain general identities between diversity and single species responses, which generalize and saturate May's stability bound. By connecting the model to systems studied in condensed matter physics, we show that the multiple equilibria regime is analogous to a critical spin-glass phase. This relation suggests new experimental ways to probe marginal stability.

Aim: In order to understand fire's impacts on vegetation dynamics, it is crucial that the distribution of fire sizes be known. We approached this distribution using a power-law distribution, which derives from self-organized criticality theory (SOC). We compute the global spatial variation in the power-law exponent and determine the main factors that explain its spatial distribution. Location: Global, at 2° grid resolution. Methods: We use satellite-derived MODIS burned-area data (MCD45) to obtain global individual fire size data for 2002-2010, grouped together for each 2° grid. A global map of fire size distribution was produced by plotting the exponent of the power law. The drivers of the spatial trends in fire size distribution, including vegetation productivity, precipitation, population density and net income, were analysed using a generalized additive model (GAM). Results: The power law gave a good fit for 93% of the global 2° grid cells with important fire activity. A global map of the fire size distribution, as approached by the power law shows strong spatial patterns. These are associated both with climatic variables (precipitation and evapotranspiration) and with anthropogenic variables (cropland cover and population density). Main conclusions: Our results indicate that the global fire size distribution changes over gradients of precipitation and aridity, and that it is strongly influenced by human activity. This information is essential for understanding potential changes in fire sizes as a result of climate change and socioeconomic dynamics. The ability to improve SOC fire models by including these human and climatic factors would benefit fire projections as well as fire management and policy.

The past decade has seen growing support for the critical brain hypothesis, i.e., the possibility that the brain could operate at or very near a critical state between two different dynamical regimes. Such critical states are well-studied in different disciplines, therefore there is potential for a continued transfer of knowledge. Here, I revisit foundations of bifurcation theory, the mathematical theory of transitions. While the mathematics is well-known it's transfer to neural dynamics leads to new insights and hypothesis.

Landscapes respond to climate, tectonic motions and sea level, but this response is mediated by sediment transport. Understanding transmission of environmental signals is crucial for predicting landscape response to climate change, and interpreting paleo‐climate and tectonics from stratigraphy. Here we propose that sediment transport can act as a nonlinear filter that completely destroys (“shreds”) environmental signals. This results from ubiquitous thresholds in sediment transport systems; e.g., landsliding, bed load transport, and river avulsion. This “morphodynamic turbulence” is analogous to turbulence in fluid flows, where energy injected at one frequency is smeared across a range of scales. We show with a numerical model that external signals are shredded when their time and amplitude scales fall within the ranges of morphodynamic turbulence. As signal frequency increases, signal preservation becomes the exception rather than the rule, suggesting a critical re‐examination of purported sedimentary signals of external forcing.

Bedform evolution and preserved cross strata are known to respond to floods. However, it is unclear if autogenic dynamics mask the flood signal in bedform evolution and cross strata. To address this, we characterize the temporal structure of autogenic noise in steady‐state bedform evolution in a physical experiment. Results reveal the existence of bedform groups—quasi‐stable collections of bedforms—that migrate at a similar speed as bedforms. We find that bedform and bedform‐group turnover timescales are the key autogenic timescales of bed evolution that set the transition time‐periods between different noise regimes in bedform evolution. Results suggest that bedform‐group turnover timescale sets the lower limit for detecting flood signals in bedform evolution, and floods with duration shorter than bedform turnover timescale can be severely degraded in bedform evolution and cross strata. Our work provides a new framework for interrogating fluvial cross strata for reconstruction of past floods. Plain Language Summary Bedforms are wavy features found regularly on the beds of rivers. Bedform deposits are the building blocks of the rock record on Earth and Mars. Bedforms and their deposits respond to floods; however, it is unclear if all floods are similarly represented in bedforms and their deposits. To address this, we identified the timescales over which bed elevation and sediment discharge are variable in a steady‐state experiment of bedform evolution using high‐resolution data. We investigated the time series of bed elevation to document the existence of bedform groups, which represent a collection of bedforms that have deep scours at their upstream and downstream end. We find that the turnover timescales (time required to move an entire land feature) of bedforms and bedform groups are the key controls on noise in bedform evolution. Results suggest that the signal of floods with duration less than bedform turnover timescale will not be found in bedform data and their deposits. However, floods with duration greater than the bedform‐group turnover timescale are likely to be expressed in bedform data and their deposits. These results provide a new theory for how floods are represented in river deposits. Key Points We show the existence of bedform groups, which are quasi‐stable collections of bedforms, previously found in aeolian dune evolution models Bedform and bedform group turnover timescales are key autogenic timescales that describe the temporal structure of noise in bed elevation Floods of duration shorter than bedform turnover timescale are expected to be unrecognizable in bed elevation and preserved cross strata

This study applies Failure Modes, Effects, and Criticality Analysis (FMECA) to evaluate and enhance the reliability of the Neusoft NeuViz Scanner, a critical medical imaging device. Collaborating across disciplines, a specialized working group comprising seasoned radiology experts and engineers undertook a rigorous two-year investigation into the intricate dynamics of gantry and console components. The discernment of critical elements such as the F102 fuse holder, left control panel card, Ucom card, DMS system, and RDC card revealed pivotal nodes requiring strategic intervention. Identifying critical components within the gantry (F102 fuse holder, DMS system, and left control panel card) and console (RDC card and database), the study calculates criticality indices based on Frequency, Severity, and Detectability. Results guide a three-tiered approach, proposing preventive, corrective, and improvement actions. Preventive measures include thorough inspections, cable evaluations, lubrication planning for the gantry, and cleaning procedures for the console. Corrective actions involve card and cable replacements. Innovative improvements encompass incorporating Uninterruptible Power Supplies, adding a second console, and implementing a voltage regulator. In the context of stringent medical device standards, this study stands as a trailblazer, aligning meticulously with ISO 14971 and navigating the intricate landscape of European Union Medical Devices Regulation standards. This unwavering commitment not only fortifies the study’s credibility but positions it as a vanguard in the realm of medical imaging. In conclusion, this research provides valuable insights into systematically evaluating and mitigating risks, offering practical strategies for improving reliability, patient safety, and adherence to medical device regulations. To the best of our knowledge, this paper is the first of its kind to employ the FMECA method for the analysis and management of failures in CT scanners.

We compute parameters characterizing many-body quantum chaos for a critical Fermi surface without quasiparticle excitations. We examine a theory of N species of fermions at nonzero density coupled to a U(1) gauge field in two spatial dimensions and determine the Lyapunov rate and the butterfly velocity in an extended random-phase approximation. The thermal diffusivity is found to be universally related to these chaos parameters; i.e., the relationship is independent of N, the gauge-coupling constant, the Fermi velocity, the Fermi surface curvature, and high-energy details.

PurposeChatbots are increasingly prevalent in the service frontline. Due to advancements in artificial intelligence, chatbots are often indistinguishable from humans. Regarding the question whether firms should disclose their chatbots' nonhuman identity or not, previous studies find negative consumer reactions to chatbot disclosure. By considering the role of trust and service-related context factors, this study explores how negative effects of chatbot disclosure for customer retention can be prevented.Design/methodology/approachThis paper presents two experimental studies that examine the effect of disclosing the nonhuman identity of chatbots on customer retention. While the first study examines the effect of chatbot disclosure for different levels of service criticality, the second study considers different service outcomes. The authors employ analysis of covariance and mediation analysis to test their hypotheses.FindingsChatbot disclosure has a negative indirect effect on customer retention through mitigated trust for services with high criticality. In cases where a chatbot fails to handle the customer's service issue, disclosing the chatbot identity not only lacks negative impact but even elicits a positive effect on retention.Originality/valueThe authors provide evidence that customers will react differently to chatbot disclosure depending on the service frontline setting. They show that chatbot disclosure does not only have undesirable consequences as previous studies suspect but can lead to positive reactions as well. By doing so, the authors draw a more balanced picture on the consequences of chatbot disclosure.