Decision making in uncertain times: what can cognitive and decision sciences say about or learn from economic crises?

B.M. was supported by a Visiting Scholar Award from the British Academy and Grant ME 3717/2 from the Deutsche Forschungsgemeinschaft (DFG) as part of the priority program ‘New Frameworks of Rationality’ (SPP 1516)

A computational fluid dynamic investigation of inhomogeneous hydrogen flame acceleration and transition to detonation

Gas explosions in homogeneous reactive mixtures have been widely studied both experimentally and numerically. However, in practice and industrial applications, combustible mixtures are usually inhomogeneous and subject to vertical concentration gradients. Limited studies have been conducted in such context which resulted in limited understanding of the explosion characteristics in such situations. The present numerical investigation aims to study the dynamics of Deflagration to Detonation Transition (DDT) in inhomogeneous hydrogen/air mixtures and examine the effects of obstacle blockage ratio in DDT. VCEFoam, a reactive density-based solver recently assembled by the authors within the frame of OpenFOAM CFD toolbox has been used. VCEFoam uses the Harten–Lax–van Leer–Contact (HLLC) scheme fr the convective fluxes contribution and shock capturing. The solver has been verified by comparing its predictions with the analytical solutions of two classical test cases. For validation, the experimental data of Boeck et al. (1) is used. The experiments were conducted in a rectangular channel the three different blockage ratios and hydrogen concentrations. Comparison is presented between the predicted and measured flame tip velocities. The shaded contours of the predicted temperature and numerical Schlieren (magnitude of density gradient) will be analysed to examine the effects of the blockage ratio on flame acceleration and DDT

Measurement of the ttbar Production Cross Section in ppbar Collisions at sqrt(s)=1.96 TeV using Lepton + Jets Events with Lifetime b-tagging

We present a measurement of the top quark pair ($t\bar{t}$) production cross section ($\sigma_{t\bar{t}}$) in $p\bar{p}$ collisions at $\sqrt{s}=1.96$ TeV using 230 pb$^{-1}$ of data collected by the D0 experiment at the Fermilab Tevatron Collider. We select events with one charged lepton (electron or muon), missing transverse energy, and jets in the final state. We employ lifetime-based b-jet identification techniques to further enhance the $t\bar{t}$ purity of the selected sample. For a top quark mass of 175 GeV, we measure $\sigma_{t\bar{t}}=8.6^{+1.6}_{-1.5}(stat.+syst.)\pm 0.6(lumi.)$ pb, in agreement with the standard model expectation.Comment: 7 pages, 2 figures, 3 tables Submitted to Phys.Rev.Let

Remotely Light-Powered Soft Fluidic Actuators Based on Plasmonic-Driven Phase Transitions in Elastic Constraint

Materials capable of actuation through remote stimuli are crucial for untethering soft robotic systems from hardware for powering and control. Fluidic actuation is one of the most applied and versatile actuation strategies in soft robotics. Here, the first macroscale soft fluidic actuator is derived that operates remotely powered and controlled by light through a plasmonically induced phase transition in an elastomeric constraint. A multiphase assembly of a liquid layer of concentrated gold nanoparticles in a silicone or styrene-ethylene-butylene-styrene elastic pocket forms the actuator. Upon laser excitation, the nanoparticles convert light of specific wavelength into heat and initiate a liquid-to-gas phase transition. The related pressure increase inflates the elastomers in response to laser wavelength, intensity, direction, and on-off pulses. During laser-off periods, heating halts and condensation of the gas phase renders the actuation reversible. The versatile multiphase materials actuate-like soft "steam engines"-a variety of soft robotic structures (soft valve, pnue-net structure, crawling robot, pump) and are capable of operating in different environments (air, water, biological tissue) in a single configuration. Tailored toward the near-infrared window of biological tissue, the structures actuate also through animal tissue for potential medical soft robotic applications

Measurement of Semileptonic Branching Fractions of B Mesons to Narrow D** States

Using the data accumulated in 2002-2004 with the DO detector in proton-antiproton collisions at the Fermilab Tevatron collider with centre-of-mass energy 1.96 TeV, the branching fractions of the decays B -> \bar{D}_1^0(2420) \mu^+ \nu_\mu X and B -> \bar{D}_2^{*0}(2460) \mu^+ \nu_\mu X and their ratio have been measured: BR(\bar{b}->B) \cdot BR(B-> \bar{D}_1^0 \mu^+ \nu_\mu X) \cdot BR(\bar{D}_1^0 -> D*- pi+) = (0.087+-0.007(stat)+-0.014(syst))%; BR(\bar{b}->B)\cdot BR(B->D_2^{*0} \mu^+ \nu_\mu X) \cdot BR(\bar{D}_2^{*0} -> D*- \pi^+) = (0.035+-0.007(stat)+-0.008(syst))%; and (BR(B -> \bar{D}_2^{*0} \mu^+ \nu_\mu X)BR(D2*0->D*- pi+)) / (BR(B -> \bar{D}_1^{0} \mu^+ \nu_\mu X)\cdot BR(\bar{D}_1^{0}->D*- \pi^+)) = 0.39+-0.09(stat)+-0.12(syst), where the charge conjugated states are always implied.Comment: submitted to Phys. Rev. Let

Challenges in QCD matter physics - The Compressed Baryonic Matter experiment at FAIR

Substantial experimental and theoretical efforts worldwide are devoted to explore the phase diagram of strongly interacting matter. At LHC and top RHIC energies, QCD matter is studied at very high temperatures and nearly vanishing net-baryon densities. There is evidence that a Quark-Gluon-Plasma (QGP) was created at experiments at RHIC and LHC. The transition from the QGP back to the hadron gas is found to be a smooth cross over. For larger net-baryon densities and lower temperatures, it is expected that the QCD phase diagram exhibits a rich structure, such as a first-order phase transition between hadronic and partonic matter which terminates in a critical point, or exotic phases like quarkyonic matter. The discovery of these landmarks would be a breakthrough in our understanding of the strong interaction and is therefore in the focus of various high-energy heavy-ion research programs. The Compressed Baryonic Matter (CBM) experiment at FAIR will play a unique role in the exploration of the QCD phase diagram in the region of high net-baryon densities, because it is designed to run at unprecedented interaction rates. High-rate operation is the key prerequisite for high-precision measurements of multi-differential observables and of rare diagnostic probes which are sensitive to the dense phase of the nuclear fireball. The goal of the CBM experiment at SIS100 (sqrt(s_NN) = 2.7 - 4.9 GeV) is to discover fundamental properties of QCD matter: the phase structure at large baryon-chemical potentials (mu_B > 500 MeV), effects of chiral symmetry, and the equation-of-state at high density as it is expected to occur in the core of neutron stars. In this article, we review the motivation for and the physics programme of CBM, including activities before the start of data taking in 2022, in the context of the worldwide efforts to explore high-density QCD matter.Comment: 15 pages, 11 figures. Published in European Physical Journal

Adaptive mechanisms of social and asocial learning in immersive collective foraging

Human cognition is distinguished by our ability to adapt to different environments and circumstances. Yet the mechanisms driving adaptive behavior have predominantly been studied in separate asocial and social contexts, with an integrated framework remaining elusive. Here, we use a collective foraging task in a virtual Minecraft environment to integrate these two fields, by leveraging automated transcriptions of visual field data combined with high-resolution spatial trajectories. Our behavioral analyses capture both the structure and temporal dynamics of social interactions, which are then directly tested using computational models sequentially predicting each foraging decision. These results reveal that adaptation mechanisms of both asocial foraging and selective social learning are driven by individual foraging success (rather than social factors). Furthermore, it is the degree of adaptivity-of both asocial and social learning-that best predicts individual performance. These findings not only integrate theories across asocial and social domains, but also provide key insights into the adaptability of human decision-making in complex and dynamic social landscapes