Calin Galeriu(CG) has commented [1] about our manuscript, "Doppler signature in electrodynamic retarded potentials" [2], that must be properly addressed, amending at the same time of notation errors [3] to clarify some formal aspects which were discussed and criticized in C. Galeriu's comments. The same author (CG) has published three papers concerning the origin of the Doppler term, v/c, in the Li\'enard-Wiechert (L-W) potentials [4-6].

The COVID-19 pandemic caused by the coronavirus had a significant effect on social, economic, and health systems globally. The virus emerged in Wuhan, China, and spread worldwide resulting in severe disease, death, and social interference. Countries implemented lockdowns in various regions to limit the spread of the virus. Some of them were successful and some failed. Here, several factors played a vital role in their success. But mostly these factors and their correlations remained unidentified. In this paper, we unlocked those factors that contributed to the success of lockdown during the COVID-19 pandemic and explored the correlations among them. Moreover, this paper proposes several strategies to control any pandemic situation in the future. Here, it explores the relationships among variables, such as population density, number of infected, death, recovered patients, and the success or failure of the lockdown in different regions of the world. The findings suggest a strong correlation among these factors and indicate that the spread of similar kinds of viruses can be reduced in the future by implementing several safety measures.

We explore the state of information disruption caused by the cocktail party effect within the framework of non-perfect information games and evolutive games with multiple werewolves. In particular, we mathematically model and analyze the effects on the gain of each strategy choice and the formation process of evolutionary stable strategies (ESS) under the assumption that the pollution risk of fake news is randomly assigned in the context of repeated dilemmas. We will develop the computational process in detail, starting with the construction of the gain matrix, modeling the evolutionary dynamics using the replicator equation, and identifying the ESS. In addition, numerical simulations will be performed to observe system behavior under different initial conditions and parameter settings to better understand the impact of the spread of fake news on strategy evolution. This research will provide theoretical insights into the complex issues of contemporary society regarding the authenticity of information and expand the range of applications of evolutionary game theory.

We solve the first-order relativistic magnetohydrodynamics (MHD) within the linear-mode analysis performed near an equilibrium configuration in the fluid rest frame. We find two complete sets of analytic solutions for the four and two coupled modes with seven dissipative transport coefficients. The former set has been missing in the literature for a long time. Our method provides a simple and general algorithm for the solution search on an order-by-order basis in the derivative expansion, and can be applied to general sets of hydrodynamic equations. We also find that the small-momentum expansions of the solutions break down when the momentum direction is nearly perpendicular to an equilibrium magnetic field due to the presence of another small quantity, that is, a trigonometric function representing the anisotropy. We elaborate on the angle dependence of the solutions and provide alternative series representations that work near the right angle. Finally, we discuss the issues of causality and stability based on our analytic solutions and recent developments in the literature.

We propose an original experimental strategy providing evidence for the first label-free disclosure of muscle actin network based on the nonresonant contribution of a multiplex CARS (M-CARS) microscopy setup. Thin actin filaments are known for being located between thick myosin filaments that can be detected in Second Harmonic Generation (SHG) with our M-CARS setup. The first step of our strategy consists in defining spatial regions where thick myosin filaments or thin actin filaments are expected. Then we present an analysis based on two M-CARS spectra corresponding to these regions, which outcome highlights differences in their relative nonresonant contribution. The final step of the presented strategy is their exploitation on each pixel of the M-CARS hyperspectral image, enabling label-free revelation of thin actin filaments. This qualitative imaging represents a proof of principle for highlighting and discriminating purposes in biological microscopy thanks to a difference in the nonlinear properties of the related proteins. Besides, this peculiar imaging technique results from a competition between several four-wave mixing schemes. This paves the way for considering label-free imaging through a competition involving several light-matter interaction effects, rather than considering several of them singularly.

The phase-out of hydro-fluorocarbons, owing to their high Global Warming Power, affecting the main gas used in Resistive Plate Chambers (RPCs), tetrafluoroethane C$_2$H$_2$F$_4$, has increased operational difficulties on existing systems and imposes strong restrictions on its use in new systems. This has motivated a new line of R\&D on sealed RPCs: RPCs that do not require a continuous gas flow for their operation and dispense the use of very complex and expensive re-circulation and/or recycling gas systems. At the moment it is not clear whether this solution can cover all fields of application normally allocated to RPCs, but it seems that it could be considered as a valid option for low particle flux triggering/tracking of particles, e.g. in cosmic ray or rare event experiments. In this work, we demonstrate the feasibility of a small telescope for atmospheric muon tracking consisting of four $300$~x~$300$~mm$^2$ sealed RPCs with gas gap widths of $1$~mm, $1.5$~mm and $2$~mm. The results suggest that it is possible to operate this type of detectors for extended periods of time (more than five months) with its main characteristics, efficiency, average charge and streamer probability, without apparent degradation and similar to a RPC operated in continuous gas flow.

Large-size hybrid and pixelized GEM-Micromegas gaseous detectors (40x40 cm$^2$ active area) were developed and installed in 2014 and 2015 for the COMPASS2 physics program which started at the same time. That program involved in particular two full years of Drell-Yan studies using a high-intensity pion beam on a thick polarized target. Although the detectors were placed behind a thick absorber, they were exposed to an important flux of low energy neutrons and photons. The detectors were designed to drastically reduce the discharge rate, a major issue for non-resistive Micromegas in high hadron flux, by a factor of more than 100 compared to the former ones. A hybrid solution was chosen where a pre-amplifying GEM foil is placed 2 mm above the micromesh electrode. A pixelized readout was also added in the center of the detector, where the beam is going through, in order to track particles scattered at very low angles. The combination of the hybrid structure and the pixelized central readout allowed the detector to be operated in an environment with particle flux above 10 MHz/cm$^2$ with very good detection efficiencies and spatial resolution. The performance has remained stable since 2015 in terms of gain and resolution, showing the interest of hybrid structures associating a GEM foil to a Micromegas board to protect gaseous detectors against discharges and aging effects

Magnetic confinement fusion reactors produce ash particles that must be removed for efficient operation. It is suggested to use autoresonance (a continuous phase-locking between anharmonic motion and a chirped drive) to remove the ash particles from a magnetic mirror, the simplest magnetic confinement configuration. An analogy to the driven pendulum is established via the guiding center approximation. The full 3D dynamics is simulated for $\alpha$ particles (the byproduct of DT fusion) in agreement with the approximated 1D model. Monte Carlo simulations sampling the phase space of initial conditions are used to quantify the efficiency of the method. The DT fuel particles are out of the bandwidth of the chirped drive and, therefore, stay in the mirror for ongoing fusion.

We show that a single nonlinear defect can thermalize an initial excitation towards a Rayleigh-Jeans (RJ) state in complex multimoded systems. The thermalization can be hindered by disorder-induced localization phenomena which drive the system into a metastable RJ state. It involves only a (quasi-)isolated set of prethermal modes and can differ dramatically from the thermal RJ. We develop a one-parameter scaling theory that predicts the density of prethermal modes and we derive the modal relaxation rate distribution, establishing analogies with the Thouless conductance. Our results are relevant to photonics, optomechanics, and cold atoms.

While many physics-based closure model forms have been posited for the sub-filter scale (SFS) in large eddy simulation (LES), vast amounts of data available from direct numerical simulation (DNS) create opportunities to leverage data-driven modeling techniques. Albeit flexible, data-driven models still depend on the dataset and the functional form of the model chosen. Increased adoption of such models requires reliable uncertainty estimates both in the data-informed and out-of-distribution regimes. In this work, we employ Bayesian neural networks (BNNs) to capture both epistemic and aleatoric uncertainties in a reacting flow model. In particular, we model the filtered progress variable scalar dissipation rate which plays a key role in the dynamics of turbulent premixed flames. We demonstrate that BNN models can provide unique insights about the structure of uncertainty of the data-driven closure models. We also propose a method for the incorporation of out-of-distribution information in a BNN. The efficacy of the model is demonstrated by a priori evaluation on a dataset consisting of a variety of flame conditions and fuels.

We report the first coupled-cluster study of Auger decay in heavy metals. The zinc atom is used as a case study due to its relevance to the Auger emission properties of the $^{67}$Ga radionuclide. Coupled-cluster theory combined with complex basis functions is used to describe the transient nature of the core-ionized zinc atom. We also introduce second-order M{\o}ller-Plesset perturbation theory as an alternative method for computing partial Auger decay widths. Scalar-relativistic effects are included in our approach for computing Auger electron energies by means of the spin-free exact two-component one-electron Hamiltonian, while spin-orbit coupling is treated by means of perturbation theory. We center our attention on the K-edge Auger decay of zinc dividing the spectrum into three parts (K-LL, K-LM, and K-MM) according to the shells involved in the decay. The computed Auger spectra are in good agreement with experimental results. The most intense peak is found at an Auger electron energy of 7432 eV, which corresponds to a $^1$D$_2$ final state arising from K-L$_2$L$_3$ transitions. Our results highlight the importance of relativistic effects for describing Auger decay in heavier nuclei. Furthermore, the effect of a first solvation shell is studied by modeling of Auger decay in the hexaaqua-zinc (II) complex. We find that K-edge Auger decay is slightly enhanced by the presence of the water molecules as compared to the bare atom.

Investigating the optimal nature of social interactions among generic actors (e.g., people or firms), aiming to achieve specifically-agreed objectives, has been the subject of extensive academic research. Using the relational models theory - comprehensively describing all social interactions among actors as combinations of only four forms of sociality: communal sharing, authority ranking, equality matching, and market pricing - the common approach within the literature revolves around qualitative assessments of the sociality models' configurations most effective in realizing predefined purposes, at times supplemented by empirical data. In this treatment, we formulate this question as a mathematical optimization problem, in order to quantitatively determine the best possible configurations of sociality forms between dyadic actors which would optimize their mutually-agreed objectives. For this purpose, we develop an analytical framework for quantifying the (meta)relational models theory, and mathematically demonstrate that combining the four sociality forms within a specific meaningful social interaction inevitably prompts an inherent tension among them, through a single elementary and universal metarelation. In analogy with financial portfolio management, we subsequently introduce the concept of Social Relations Portfolio (SRP) management, and propose a generalizable procedural methodology capable of quantitatively identifying the efficient SRP for any objective involving meaningful social relations. As an important illustration, the methodology is applied to the Triple Bottom Line paradigm to derive its efficient SRP, guiding practitioners in precisely measuring, monitoring, reporting and (proactively) steering stakeholder management efforts regarding Corporate Social Responsibility (CSR) and Environmental, Social and Governance (ESG) within and / or across organizations.

Understanding the relation between cortical neuronal network structure and neuronal activity is a fundamental unresolved question in neuroscience, with implications to our understanding of the mechanism by which neuronal networks evolve over time, spontaneously or under stimulation. It requires a method for inferring the structure and composition of a network from neuronal activities. Tracking the evolution of networks and their changing functionality will provide invaluable insight into the occurrence of plasticity and the underlying learning process. We devise a probabilistic method for inferring the effective network structure by integrating techniques from Bayesian statistics, statistical physics and principled machine learning. The method and resulting algorithm allow one to infer the effective network structure, identify the excitatory and inhibitory nature of its constituents, and predict neuronal spiking activities by employing the inferred structure. We validate the method and algorithm's performance using synthetic data, spontaneous activity of an in silico emulator and realistic in vitro neuronal networks of modular and homogeneous connectivity, demonstrating excellent structure inference and activity prediction. We also show that our method outperforms commonly used existing methods for inferring neuronal network structure. Inferring the evolving effective structure of neuronal networks will provide new insight into the learning process due to stimulation in general and will facilitate the development of neuron-based circuits with computing capabilities.

Stellarator plasmas are externally controlled to a degree unparalleled by any other fusion concept, magnetic or inertial. This control is largely through the magnetic fields produced by external coils. The development of fusion energy could be expedited by carrying out remarkably straight-forward computations to define strategies for exploiting this external control. In addition to these computations, which have a reliability limited only by competence, certain physics areas that affect the develop of stellarator power plants should have more intense study. The low cost and speed with which computations can be carried out relative to experiments has implications for the development of fusion. Computations should be used to develop a strategy that to the extent possible allows major issues to be circumvented. Required computations for this strategy are the subject of this paper.

This study investigates the dynamics of social contagion within hypergraphs, reflecting the modern intricacies of group influence on individual decision-making as magnified by social media platforms. We introduce a novel social contagion model that captures the nuanced interplay between individual caution in decision-making (\(\phi_k\)) and the efficiency of information transmission within group interactions (\(\phi_m\)). By integrating dual-threshold mechanisms, our model delineates the conditions under which individuals assimilate group behaviors, opinions, or attitudes. Our findings reveal a critical balance in the transmission of information, with higher individual thresholds (\(\phi_k\)) necessitating greater group consensus (\(\phi_m\)) to precipitate a global cascade. The model underscores the pivotal role of group interactions in social contagion processes, challenging traditional contagion models that focus on pairwise interactions. We demonstrate the utility of our model through extensive simulations, revealing that the collective dynamics of social contagion are significantly influenced by the structural parameters of the underlying hypergraph. The implications of our work extend to a variety of domains, from marketing to public health, offering a robust framework to better understand and leverage the phenomena of social contagion in an increasingly interconnected world.

Vibrational resonance (VR) is a nonlinear phenomenon in which the system response to a weak signal can be resonantly enhanced by applying a high-frequency modulation signal with an appropriate amplitude. The majority of VR research has focused on amplifying the amplitude or intensity of the system response to a weak signal, whereas the study of the phase information of system responses in VR remains limited. Here, we investigate the VR phenomena in both amplitude and phase quadratures of an optical field in a Kerr nonlinear cavity driven by a near-resonant weak signal and a far-detuned modulation signal. Analytical and numerical results demonstrated that the resonant enhancement in the amplitude and phase quadratures of the system response to a weak signal simultaneously occurs as the amplitude of the modulation signal is varied. There is a linear relation between the amplitude and frequency of the modulation signal for achieving an optimal VR effect. Furthermore, we generalized our study to investigate the quadrature at an arbitrary phase and determined that the VR enhancement sensitively depends on the phase. Our findings not only broaden the scope of VR research by incorporating phase information but also introduces an approach for amplifying an optical field by manipulating another optical field.

Within the scope of reacting flow simulations, the real-time direct integration (DI) of stiff ordinary differential equations (ODE) for the computation of chemical kinetics stands as the primary demand on computational resources. Meanwhile, as the number of transport equations that need to be solved increases, the computational cost grows more substantially, particularly for those combustion models involving direct coupling of chemistry and flow such as the transported probability density function model. In the current study, an integrated Graphics Processing Unit-Artificial Neural Network (GPU-ANN) framework is introduced to comply with heavy computational costs while maintaining high fidelity. Within this framework, a GPU-based solver is employed to solve partial differential equations and compute thermal and transport properties, and an ANN is utilized to replace the calculation of reaction rates. Large eddy simulations of two swirling flames provide a robust validation, affirming and extending the GPU-ANN approach's applicability to challenging scenarios. The simulation results demonstrate a strong correlation in the macro flame structure and statistical characteristics between the GPU-ANN approach and the traditional Central Processing Unit (CPU)-based solver with DI. This comparison indicates that the GPU-ANN approach is capable of attaining the same degree of precision as the conventional CPU-DI solver, even in more complex scenarios. In addition, the overall speed-up factor for the GPU-ANN approach is over two orders of magnitude. This study establishes the potential groundwork for widespread application of the proposed GPU-ANN approach in combustion simulations, addressing various and complex scenarios based on detailed chemistry, while significantly reducing computational costs.

The mobility of externally-driven phoretic propulsion of particles is evaluated by simultaneously solving the solute conservation equation, interaction potential equation, and the modified Stokes equation. While accurate, this approach is cumbersome, especially when the interaction potential decays slowly compared to the particle size. In contrast to external phoresis, the motion of self-phoretic particles is typically estimated by relating the translation and rotation velocities with the local slip velocity. While this approach is convenient and thus widely used, it is only valid when the interaction decay length is significantly smaller than the particle size. Here, by employing the Lorentz reciprocal theorem, we combine the benefits of two approaches and derive unified mobility expressions with arbitrary interaction potentials such that the expressions predict the translation and rotation velocities for both externally driven and self-propelling particles. We show that these expressions can conveniently recover the well-known mobility relationships of external electrophoresis and diffusiophoresis for arbitrary double-layer thickness. Additionally, we show that for a spherical microswimmer, our derived expressions relax to the slip velocity calculations in the limit of thin interaction lengthscales. We also employ the derived mobility expressions to calculate the velocities of an autophoretic Janus particle. We find that there is significant dampening in the translation velocity even when the interaction length is an order of magnitude larger than the particle size. Finally, we study the motion of a catalytically self-propelled particle, while it also propels due to external concentration gradients, and demonstrate how the two propulsion modes compete with each other.

An unprecedented organic Fermat spiral optical waveguide (FSOW) self transducing green fluorescence is fabricated using a pseudo-plastic (E)-1-(((5-bromopyridin-2-yl)imino)methyl)naphthalene-2-ol crystal. A 1.618-millimeter-long crystal is initially bent into a hairpin-like bent waveguide. Later, a meticulous mechanophotonic strategy is employed to sculpt the hairpin-like bent waveguide into the Fermat spiral geometry, covering a compact area of 330x238 um2. The optical signal in FSOW survives two sharp 180-degree turns to produce optical output. The remarkably low bending-induced optical loss in FSOW can be ascribed to the smooth-defect-free surface morphology of the crystal. The development of such versatile optical components capable of transducing light through sharp bends is pivotal for realizing large-scale all-organic photonic circuits.

In the domain of mechanophotonics, achieving real-time applicability of organic crystals in visible light communication (VLC) technologies necessitates affordable light-emitting diodes (LEDs) as sources of light to run photonic devices through sustainable methods. Here in, we demonstrate an efficient strategy to excite (Z)-3-(3',5'-bis(trifluoromethyl)-[1,1'-biphenyl]-4-yl)-2-(4-methoxyphenyl) acrylonitrile (CF3OMe), 9,10-bis(phenylethynyl)anthracene (BPEA) and 2,2'-((1E,1'E)-hydrazine-1,2-diylidenebis(methaneylylidene))diphenol (SAA) flexible crystal waveguides utilizing UV LED source and transduce respective blue, orange and yellow fluorescence signals. The capability of the focused LED lies in its ability to (i) energize mechanically bent crystals at an angle of 180{\deg}, (ii) evanescently excite the FL of a SAA waveguide using the FL of CF3OMe waveguide through energy transfer, and (iii) excite and split different signals in a 2X2 hybrid directional coupler based on SSA-BPEA crystals. These demonstrations underscore the practicality of the proposed technique for sustainable applications in photonic systems related to VLC.

Interest in magnetically induced (MI) transitions of alkali metal atoms is caused by the fact that their intensities can exceed the intensities of regular atomic transitions in a wide range of magnetic field (200 - 4000 G). The goal of this work was to form and study, for the first time, an electromagnetically induced transparency (EIT) resonance in a strong magnetic field using a probe radiation tuned to |Fg = F; mF = 0-> Fe = F; mF = 0> MI transition, which is forbidden in zero magnetic field. Two narrow-band linearly-polarized cw diode lasers were used to form a EIT resonance on a lambda-type system of Cs atomic D2 line in a strong transverse magnetic field (up to 1000 G). The resonance was formed in Cs atomic vapor nanocell with the atomic vapor column thickness of 850 nm.

Miniaturized on-chip spectrometers with small footprints, lightweight, and low cost are in great demand for portable optical sensing, lab-on-chip systems, and so on. Such miniaturized spectrometers are usually based on engineered spectral response units and then reconstruct unknown spectra with algorithms. However, due to the limited footprints of computational on-chip spectrometers, the recovered spectral resolution is limited by the number of integrated spectral response units/filters. Thus, it is challenging to improve the spectral resolution without increasing the number of used filters. Here we present a computational on-chip spectrometer using electrochromic filters that can be electrochemically modulated to increase the efficient sampling number for higher spectral resolution. These filters are directly integrated on top of the photodetector pixels, and the spectral modulation of the filters results from redox reactions during the dual injection of ions and electrons into the electrochromic material. We experimentally demonstrate that the spectral resolution of the proposed spectrometer can be effectively improved as the number of applied voltages increases. The average difference of the peak wavelengths between the reconstructed and the reference spectra decreases from 14.48 nm to 2.57 nm. We also demonstrate the proposed spectrometer can be worked with only four or two filter units, assisted by electrochromic modulation. This strategy suggests a new way to enhance the performance of miniaturized spectrometers with tunable spectral filters for high resolution, low-cost, and portable spectral sensing, and would also inspire the exploration of other stimulus responses such as photochromic and force-chromic, etc, on computational spectrometers.

Optical skyrmions formed in terms of polarization are topological quasi-particles and have garnered much interest in the optical community owing to their unique inhomogeneous polarization structure and simplicity in their experimental realization. These structures belong to the Poincar\'e beams satisfying the stable topology. We theoretically investigated the non-diffracting and self-healing Poincar\'e beams based on the superposition of two orthogonal Bessel modes by mode matching technique. These Poincar\'e beams are topologically protected, and we suggest them as optical skyrmions. These optical skyrmions are quasi-skyrmions and their range of propagation depends on the range of superposed Bessel modes. The polarization structure of these optical skyrmions has no change upon the propagation. A necessary experimental configuration is suggested to realize variable order skyrmions in Bessel modes experimentally. This work can provide a new direction for the generation of skyrmions with completely new textures and features with respect to existing skyrmions originating from Laguerre-Gaussian modes.

A uniform solidification front undergoes non-trivial deformations when encountering an insoluble dispersed particle in a melt. For solid particles, the overall deformation characteristics are primarily dictated by heat transfer between the particle and the surroundings, remaining unaffected by the rate of approach of the solidification front. In this Letter, we show that, conversely, when interacting with a droplet or a bubble, the deformation behaviour exhibits entirely different and unexpected behaviour. It arises from an interfacial dynamics which is specific to particles with free interfaces, namely thermal Marangoni forces. Our study employs a combination of experiments, theory, and numerical simulations to investigate the interaction between the droplet and the freezing front and unveils its surprising behaviour. In particular, we quantitatively understand the dependence of the front deformation $\Delta$ on the front propagation velocity, which, for large front velocities, can even revert from attraction ($\Delta < 0$) to repulsion ($\Delta > 0$).

Microvortex generators (MVGs) are a promising solution to control shock wave/turbulent boundary layer interactions (SBLIs). This study examines the effects of a microramp VG on an SBLI generated by an oblique shock wave and a turbulent boundary layer using direct numerical simulations (DNSs). Two cases, with and without MVGs, are compared at free-stream Mach number equal to 2 and friction Reynolds number equal to 600. A long integration period allows assessing how MVGs affect the typical SBLI low-frequency unsteadiness. The 3D microramp wake dramatically alters the interaction, inducing spanwise modulation and topology changes of the separation. For example, tornado-like structures redistribute the recirculating flow in the spanwise and wall-normal directions. The increase in momentum close to the wall by the ramp vortices delays the onset of the separation but also leads to an increase in the intensity of the wall-pressure fluctuations. We then characterise the interaction between the arch-like vortices around the ramp wake and the SBLI. The spanwise vorticity shows that these vortices follow the edge of the separation, and their intensity is unaffected by the shocks. The shocks, instead, are deformed by the impingement of the vortices, although the spectral analysis does not reveal relevant traces of their shedding frequency at separation. These vortices, however, may be important in the separation bubble closure. A constant increase - in both value and magnitude - in the low-frequency peak is observed all along the span, suggesting that the shock oscillation remains coherent while being disturbed by the arch-like vortices.

Ultrafast laser sources in the far ultraviolet (100 nm to 300 nm) have been the subject of intense experimental efforts for several decades, driven primarily by the requirements of advanced experiments in ultrafast science. Resonant dispersive wave emission from high-energy laser pulses undergoing soliton self-compression in a gas-filled hollow capillary fibre promises to meet several of these requirements for the first time, most importantly by combining wide-ranging wavelength tuneability with the generation of extremely short pulses. In this Perspective, we give an overview of this approach to ultrafast far-ultraviolet sources, including its historical origin and underlying physical mechanism, the state of the art and current challenges, and our view of potential applications both within and beyond ultrafast science.

The exquisite precision of atom interferometers has sparked the interest of a large community for use cases ranging from fundamental physics to geodesy and inertial navigation. However, their practical use for onboard applications is still limited, not least because rotation and acceleration are intertwined in a single phase shift in free-fall atom interferometers, which makes the extraction of a useful signal more challenging. Moreover, the spatial separation of the wave packets due to rotations leads to a loss of signal. Here we present an atom interferometer operating over a large range of random angles, rotation rates and accelerations. An accurate model of the expected phase shift allows us to untangle the rotation and acceleration signals. We also implement a real-time compensation system using two fibre-optic gyroscopes and a tip-tilt platform to rotate the reference mirror and maintain the full contrast of the atom interferometer. Using these theoretical and practical tools, we reconstruct the fringes and demonstrate a single-shot sensitivity to acceleration of 24 $\mu$g, for a total interrogation time of 2T = 20 ms, for angles and rotation rates reaching 30$^\circ$ and 14 $^\circ$/s respectively. Our hybrid rotating atom interferometer unlocks the full potential of quantum inertial sensors for onboard applications, such as autonomous navigation or gravity mapping.

Optical spectroscopy plays an essential role across scientific research and industry for non-contact materials analysis1-3, increasingly through in-situ or portable platforms4-6. However, when considering low-light-level applications, conventional spectrometer designs necessitate a compromise between their resolution and sensitivity7,8, especially as device and detector dimensions are scaled down. Here, we report on a miniaturizable spectrometer platform where light throughput onto the detector is instead enhanced as the resolution is increased. This planar, CMOS-compatible platform is based around metasurface encoders designed to exhibit photonic bound states in the continuum9, where operational range can be altered or extended simply through adjusting geometric parameters. This system can enhance photon collection efficiency by up to two orders of magnitude versus conventional designs; we demonstrate this sensitivity advantage through ultra-low-intensity fluorescent and astrophotonic spectroscopy. This work represents a step forward for the practical utility of spectrometers, affording a route to integrated, chip-based devices that maintain high resolution and SNR without requiring prohibitively long integration times.

We propose a stochastic model to describe the dynamics of contact duration in face to face interactions. It reproduces precisely the deviations from the mean-time that were found to be statistically very similar in several environments. The model is based on geometric graphs built from points of a Levy walk. It has a single parameter, the Levy index, that is found to be $\alpha\simeq1.1$ on all the datasets. In this model, the duration of contacts in face to face interactions, which corresponds to the length of discussions for humans, is related to the size of the connected components (clusters) of the Levy graph. We compute analytically the distribution of the clusters size which is of stretched-exponential type and reveals the dynamics of the process. A discussion starts about a topic that is first developed, which, in the Levy framework is described by a geometric distribution. It is then driven by a preferential attachment mechanism; the more a discussion lasts, the more it is likely to continue since there are more points to come back to. This leads to heavy tails in the contact duration. The combined behavior is well captured by the formation of clusters in Levy graphs. We show it has some similarities with the case of a growing network with a sub-linear power-law kernel. More precisely, in this model discussions appear as an aggregation process with a natural scale fixed by the mean interaction time between the individuals.

In high-density crowds, local motion can propagate, amplify, and lead to macroscopic phenomena, including 'density waves'. These density waves only occur when individuals interact, and impulses are transferred to neighbours. How this impulse is passed on by the human body and which effects this has on individuals is still not fully understood. To further investigate this, experiments focusing on the propagation of a push were conducted. In the experiments the crowd is greatly simplified by five people lining up in a row. The rearmost person in the row was pushed forward in a controlled manner with a punching bag. The intensity of the push, the initial distance between participants and the initial arm posture were varied. Collected data included side view and top view video recordings, head trajectories, 3D motion using motion capturing (MoCap) suits as well as pressure measured at the punching bag. With a hybrid tracking algorithm, the MoCap data are combined with the head trajectories to allow an analysis of the motion of each limb in relation to other persons. The observed motion of the body in response to the push can be divided into three phases. These are (i) receiving an impulse, (ii) receiving and passing on an impulse, and (iii) passing on an impulse. Using the 3D MoCap data, we can identify the start and end times of each phase. To determine when a push is passed on, the forward motion of the person in front has to be considered. The projection of the center of mass relative to the initial position of the feet is a measure of the extent to which a person is displaced from the rest position. Specifying the timing of these phases is particularly important to distinguish between different types of physical interactions. Our results contribute to the development and validation of a pedestrian model for identifying risks due to motion propagation in dense crowds.

Hydrogen and carbon dioxide transport can both play an essential role in climate-neutral energy systems. Hydrogen networks help serve regions with high energy demand, while excess emissions are transported away in carbon dioxide networks. For the synthesis of carbonaceous fuels, it is less clear which input should be transported: hydrogen to carbon point sources or carbon to low-cost hydrogen. We explore both networks' potential synergies and competition in a cost-optimal carbon-neutral European energy system. In direct comparison, a hydrogen network is more cost-effective than a carbon network, since it serves to transport hydrogen to demand and to point source of carbon for utilization. However, in a hybrid scenario where both networks are present, the carbon network effectively complements the hydrogen network, promoting carbon capture from biomass and reducing reliance on direct air capture. Our analysis suggests integrating hydrogen and carbon dioxide networks into European energy policy for a robust, carbon-neutral or carbon-negative future energy system.

We report the first experimental results obtained with the new haloscope of the QUAX experiment located at Laboratori Nazionali di Frascati of INFN (LNF). The haloscope is composed of a OFHC Cu resonant cavity cooled down to about 30 mK and immersed in a magnetic field of 8 T. The cavity frequency was varied in a 6 MHz range between 8.831496 and 8.83803 GHz. This corresponds to a previously unprobed mass range between 36.52413 and 36.5511 $\mu$eV. We don't observe any excess in the power spectrum and set limits on the axion-photon coupling in this mass range down to $g_{a\gamma\gamma} < 0.861 \times 10^{-13}$ GeV$^{-1}$ with the confidence level set at $90\%$.

This paper presents a new homogenized model of two-component electrolyte transport through a weakly piezoelectric porous medium. The model relevant to the microscopic scale describes quasi-stationary states of the medium while reflecting essential physical phenomena, such as electrochemical interactions in a dilute Newtonian solvent under assumptions of slow flow. The dimensional analysis of the mathematical model introduces scaling of the viscosity, electric permittivity, piezoelectric coupling, and dielectric tensor. The micromodel is linearized around the reference state represented by the thermodynamic equilibrium and further upscaled through the asymptotic homogenization method. Due to the scaling of parameters, the derived limit model retains the characteristic length associated with the pore size and the electric double-layer thickness. The upscaling procedure gives a fully coupled macroscopic model describing the electrolyte flow in terms of a global pressure and streaming potentials of the two ionic species in the weakly piezoelectric matrix. By virtue of the characteristic responses, quantities of interest are reconstructed at the microscopic scale using the resolved macroscopic fields. The coupling between electrochemical and mechanical phenomena influenced by the skeleton piezoelectricity is illustrated using numerical examples. The model, which was motivated by the cortical bone porous tissue, is widely applicable in designing new biomaterials involving piezoelectric stimulation.

How the solar wind influences the magnetospheres of the outer planets is a fundamentally important question, but is difficult to answer in the absence of consistent, simultaneous monitoring of the upstream solar wind and the large-scale dynamics internal to the magnetosphere. To compensate for the relative lack of in-situ data, propagation models are often used to estimate the ambient solar wind conditions at the outer planets for comparison to remote observations or in-situ measurements. This introduces another complication: the propagation of near-Earth solar wind measurements introduces difficult-to-assess uncertainties. Here, we present the Multi-Model Ensemble System for the outer Heliosphere (MMESH) to begin to address these issues, along with the resultant multi-model ensemble (MME) of the solar wind conditions near Jupiter. MMESH accepts as input any number of solar wind models together with contemporaneous in-situ spacecraft data. From these, the system characterizes typical uncertainties in model timing, quantifies how these uncertainties vary under different conditions, attempts to correct for systematic biases in the input model timing, and composes a MME with uncertainties from the results. For the case of the Jupiter-MME presented here, three solar wind propagation models were compared to in-situ measurements from the near-Jupiter spacecraft Ulysses and Juno which span diverse geometries and phases of the solar cycle, amounting to more than 14,000 hours of data over 2.5 decades. The MME gives the most-probable near-Jupiter solar wind conditions for times within the tested epoch, outperforming the input models and returning quantified estimates of uncertainty.

Using the PTB Ion Counter nanodosimeter at the Heidelberg Ion-Beam Therapy Center (HIT), the track structure (TS) of therapeutic carbon ions (C12) after penetrating a layer of simulated tissue was investigated for the first time. Experiments performed at different positions of the pristine C12 Bragg peak were reported in the first part of the paper. In this part, simulations with Geant4 are reported, for investigating the composition of the radiation field in the nanodosimeter and provide input data for TS simulations using the PTra code. The focus is on comparing these simulations with a second set of experiments aimed at assessing the impact of different secondary particle backgrounds on measurements at the same C12 energy in the detector. The Geant4 simulations show that the mean energy of C12 in the nanodosimeter was between 120 MeV and 320 MeV lower than the value of 1 GeV predicted by SRIM. While these findings align with the observed change in the mean measured ionization cluster size ($M_1$) for C12 traversing the nanodosimeter, TS simulations yield much lower values for the expected $M_1$ under these conditions. The TS simulations further indicate that the observed enhanced $M_1$ for C12 passing the target at a nonzero impact parameter is largely due to secondary heavy-charged particles. A smaller contribution to this background comes from C12 missing the nanodosimeter's trigger detector in the experiments. The results of the Geant4 simulations show that only about a third of the C12 traversing the nanodosimeter hit the trigger detector. The background owing to hidden coincidences between C12 registered in the trigger detector and those missing it or secondary heavy-charged particles from different events only explains the enhanced $M_1$ in the penumbra of the ion tracks but not the difference observed when C12 traverse the target volume.

The study of matter at extreme densities and temperatures has emerged as a highly active frontier at the interface of plasma physics, material science and quantum chemistry with direct relevance for planetary modeling and inertial confinement fusion. A particular feature of such warm dense matter is the complex interplay of strong Coulomb interactions, quantum effects, and thermal excitations, rendering its rigorous theoretical description a formidable challenge. Here, we report a breakthrough in path integral Monte Carlo simulations that allows us to unravel this intricate interplay for light elements without nodal restrictions. This new capability gives us access to electronic correlations previously unattainable. As an example, we apply our method to strongly compressed beryllium to describe x-ray Thomson scattering (XRTS) data obtained at the National Ignition Facility. We find excellent agreement between simulation and experiment. Our analysis shows an unprecedented level of consistency for independent observations without the need for any empirical input parameters.

We investigate the transformation and amplification of a light wave with phase velocity $v$ and frequency $\omega$ propagating through an optical waveguide and a racetrack resonator parametrically modulated by a travelling wave with phase velocity $v_p$ and frequency $\omega_p \ll \omega$. We consider the propagation within a small bandwidth $\Delta\omega \ll \omega$ where the waveguide is assumed to be dispersionless and low loss. Close to the luminal condition, $v_p=v$, the bandwidth of the output frequency comb spectrum, as well as the propagation nonreciprocity, can significantly grow. However, for realistic low-loss optical waveguides and resonators, the light amplification remains small due to its essentially broadband character. For a lithium niobate waveguide modulated with a $0.1\%$ relative refractive index amplitude at frequency $\omega_p \sim 100$ GHz, the amplification of the light power integrated over the frequency band $\Delta\omega \ll \omega \sim 200$ THz is estimated as $\sim 1$ dB/m.

The formation of small droplets and bubbles in turbulent flows is a crucial process in geophysics and engineering, whose underlying physical mechanism remains a puzzle. In this letter, we address this problem by means of high-resolution numerical simulations, comparing a realistic multiphase configuration with a numerical experiment in which we attenuate the presence of strong velocity gradients either across the whole mixture or in the disperse phase only. Our results show unambiguously that the formation of small droplets is governed by the internal dynamics which occur during the break-up of large drops and that the high vorticity and the extreme dissipation associated to these events are the consequence and not the cause of the breakup.

Second harmonic generation (SHG) in van der Waals (vdWs) materials has garnered significant attention due to its potential for integrated nonlinear optical and optoelectronic applications. Stacking faults in vdWs materials, a typical kind of planar defect, can introduce a new degree of freedom to modulate the crystal symmetry and resultant SHG response, however, the physical origin and tunability of stacking-fault-governed SHG in vdWs materials remain unclear. Here, taking the intrinsically centrosymmetric vdWs RhI3 as an example, we theoretically reveal the origin of stacking-fault-governed SHG response, where the SHG response comes from the energetically favorable AC- Cstacking fault of which the electrical transitions along the high symmetry paths Gamma-M and Gamma-K in the Brillion zone play the dominant role at 810 nm. Such stacking-fault-governed SHG response is further confirmed via structural characterizations and SHG measurements. Furthermore, by applying hydrostatic pressure on RhI3, the correlation between structural evolution and SHG response is revealed with SHG enhancement up to 6.9 times, where the decreased electronic transition energies and huger momentum matrix elements due to the stronger interlayer interactions upon compression magnify the SHG susceptibility. This study develops a promising foundation based on strategically designed stacking faults for pioneering new avenues in nonlinear nano-optics.

A novel type of resistive plate chamber, based on diamond-like carbon (DLC) electrodes is under development for background identification in the MEG II experiment. The DLC-RPC is required to have a radiation hardness to mass irradiation since it is planned to be placed in a high-intensity and low-momentum muon beam. In this study, the aging test using a high-intensity X-ray beam was conducted to evaluate the radiation hardness of the DLC-RPC. The accumulated charge due to X-ray irradiation reached about 54 C/cm$^2$, which is approximately half of the one-year irradiation dose expected in the MEG II experiment. As a result, the degradation of the gas gain was observed due to fluorine deposition and insulators formed on the DLC electrodes. In addition, discharges via spacers were also observed repeatedly and interrupted the DLC-RPC operation.

A novel turbulence control strategy for wall-bounded shear flow is proposed by Chagelishvili et al, 2014. The essence of this strategy involves continuously imposition of specially designed seed velocity perturbations with spanwise asymmetry near the flow wall. The configuration of this imposed velocity field, enhanced due to the shear flow non-normality, breaks the flow spanwise reflection symmetry, specifically, resulting in the generation of a secondary nonuniform spanwise mean flow. Consequently, this secondary flow significantly reduces flow turbulence. In Chagelishvili et al, 2014, the first steps were taken towards developing this new turbulence control strategy and demonstrating its efficiency. The plane Couette flow was considered, as a representative example, and a theoretical and hypothetical weak near-wall volume forcing was designed, which though theoretical, provided valuable insights into the characteristics of the seed velocity field. Obviously, the practical significance of this control strategy should be confirmed by evaluating its effectiveness in flows at various Reynolds numbers, and with different parameters of the imposed seed velocity field. In this paper, we investigate the effectiveness and universality of the turbulence control strategy for Couette flow at various Reynolds numbers and different locations of the volume forcing. Through direct numerical simulations, we show universality of the discussed turbulence control method. The application of a specially designed, weak near-wall volume forcing with a fixed configuration and amplitude results in the same effective turbulence control, reducing turbulence kinetic energy production by 30-40\% across a wider range Reynolds numbers, $Re_{\tau} = 52,92,128,270$ and various localizations.

The structure of a complex network plays a crucial role in determining its dynamical properties. In this work, we show that the directed, hierarchical organisation of a network causes the system to break detailed balance and dictates the production of entropy through non-equilibrium dynamics. We consider a wide range of dynamical processes and show how different directed network features govern their thermodynamics. Next, we analyse a collection of 97 empirical networks and show that strong directedness and non-equilibrium dynamics are both ubiquitous in real-world systems. Finally, we present a simple method for inferring broken detailed balance and directed network structure from multivariate time-series and apply our method to identify non-equilibrium and hierarchical organisation in both human neuroimaging and financial time-series. Overall, our results shed light on the thermodynamic consequences of directed network structure and indicate the importance and ubiquity of hierarchical organisation and non-equilibrium dynamics in real-world systems.

Small-scale turbulence can be comprehensively described in terms of velocity gradients, which makes them an appealing starting point for low-dimensional modeling. Typical models consist of stochastic equations based on closures for non-local pressure and viscous contributions. The fidelity of the resulting models depends on the accuracy of the underlying modeling assumptions. Here, we discuss an alternative data-driven approach leveraging machine learning to derive a velocity gradient model which captures its statistics by construction. We use a normalizing flow to learn the velocity gradient probability density function (PDF) from direct numerical simulation (DNS) of incompressible turbulence. Then, by using the equation for the single-time PDF of the velocity gradient, we construct a deterministic, yet chaotic, dynamical system featuring the learned steady-state PDF by design. Finally, utilizing gauge terms for the velocity gradient single-time statistics, we optimize the time correlations as obtained from our model against the DNS data. As a result, the model time realizations statistically closely resemble the time series from DNS.

We present a comparison between interband cascade lasers (ICLs) with a 6-stage active region emitting at 5 {\mu}m with AlSb/InAs superlattice claddings and bulk Al_0.85 Ga_0.15 As_0.07 Sb_0.93 claddings. Utilizing bulk AlGaAsSb claddings with its lower refractive index compared to the mostly used AlSb/InAs superlattice claddings, the mode-confinement in the active region increases by 14.4 % resulting in an improvement of the lasing threshold current density. For broad area laser and under pulsed excitation, the ICL with AlGaAsSb claddings shows a lower threshold current density of J_th=396 A/cm^2 compared to J_th=521 A/cm^2 for the reference ICL with superlattice claddings. Additionally, a higher characteristic temperature was obtained for the ICL with bulk claddings. A measured pulsed operation is observed up to 65 C.

Backbone-assisted assembly processes -- such as protein folding -- allow the assembly of a large number of structures with high accuracy from only a small handful of fundamental building blocks. We aim to explore general principles underlying this phenomenon by studying variants of the temperature-1 abstract tile assembly model (aTAM). We consider the existence of finite sets of tile types that can deterministically assemble any shape producible by a given assembly model; we call such tile type sets universal assembly kits. Our first model, which we call the ``backboned aTAM", generates backbone-assisted assembly by forcing tiles to be be added to lattice positions neighbouring the immediately preceding tile, using a predetermined sequence of tile types. We demonstrate the existence of universal assembly kit for the backboned aTAM, and show that the existence of this set is maintained even under stringent restrictions to the rules of assembly. We hypothesise that the advantage of the backboned aTAM relative to the conventional aTAM -- which does not possess such a set -- is in part due to the specification of a sequence, and in part due to the geometric restrictions imposed by the backbone. To explore this intuition, we develop a second model call the ``sequenced aTAM". The sequenced aTAM uses a predetermined sequence of tiles, but does not constrain a tile to neighbour the immediately preceding tiles. We prove that this model has no universal assembly kit. The lack of such a kit is surprising, given that the number of tile sequences of length $N$ scales faster than the number of producible shapes of size $N$ for a sufficiently large -- but finite -- set of tiles.

Consider an internally heated fluid between parallel plates with fixed thermal fluxes. Can the upward convective heat transport due to turbulence be so effective that the top plate is on average hotter than the bottom one, causing a downward conductive heat flux? For a large class of heat sources that vary in the direction of gravity, we prove that the average temperature difference between the bottom and top plates satisfies $\langle \delta T \rangle_h \geq \sigma R^{-1/3} - \mu$, where $R$ is a `flux' Rayleigh number and the constants $\sigma,\mu >0$ depend on the geometric properties of the internal heating. This lower bound implies that $\langle \delta T \rangle_h \geq 0$ for all Rayleigh numbers smaller than a critical value $R_o$, revealing regimes in which mean downward conduction is impossible. The critical Rayleigh number $R_o$ depends on the heating distribution and can be made arbitrarily large by concentrating the heating near the bottom plate. However, there is no heat distribution for which our lower bound rules out downward conduction at all Rayleigh numbers. This points to a fundamental difference between internally heated convection and its limiting case of Rayleigh-B\'enard convection with fixed flux boundary conditions, for which $\langle \delta T \rangle_h$ is known to remain positive.

Hybrid organometal halide perovskites (HP) present exceptional optoelectronic properties, but their poor long-term stability is a major bottleneck for their commercialization. Herein, we present a solvent-free approach to growing single-crystal organic nanowires (ONW), nanoporous metal oxide scaffolds, and HP to form a core@multishell architecture. The synthetic procedure is carried out under mild vacuum conditions employing thermal evaporation for the metal-free phthalocyanine (H2Pc) nanowires, which will be the core, plasma-enhanced chemical vapor deposition (PECVD) for the TiO2 shell, and co-evaporation of lead iodide (PbI2) and methylammonium iodide (CH3NH3I / MAI) for the CH3NH3PbI3 (MAPbI3 / MAPI) perovskite shell. We present a detailed characterization of the nanostructures by (S)-TEM and XRD, revealing a different crystallization of the hybrid perovskite depending on the template: while the growth on H2Pc nanowires induces the typical tetragonal structure of the MAPI perovskite, a low-dimensional phase (LDP) was observed on the one-dimensional TiO2 nanotubes. Such a combination yields an unprecedentedly stable photoluminescence emission over 20 hours and over 300 hours after encapsulation in polymethyl methacrylate (PMMA) under different atmospheres including N2, air, and high moisture levels. In addition, the unique one-dimensional morphology of the system, together with the high refractive index HP, allows for a strong waveguiding effect along the nanowire length.

We have computed the linear optical absorption spectra of three graphene quantum dots (GQDs), saturated by hydrogens on the edges, using both first-principles time-dependent density-functional theory (TDDFT) and the Pariser-Parr-Pople (PPP) model coupled with the configuration-interaction (CI) approach. To understand the influence of electron-correlation effects, we have also calculated the singlet-triplet energy gap (spin gap) of the three GQDs. Because of the presence of edge hydrogens, these GQDs are effectively polycyclic aromatic hydrocarbons (PAHs) dibenzo[bc,ef]coronene (also known as benzo(1,14)bisanthene, C$_{30}$H$_{14}$), and two isomeric compounds, dinaphtho[8,1,2abc;2,1,8klm]coronene and dinaphtho[8,1,2abc;2,1,8jkl]coronene with the chemical formula C$_{36}$H$_{16}$. The two isomers have different point group symmetries, $C_{2v}$, and $C_{2h}$, therefore, this study will also help us understand the influence of symmetry on optical properties. A common feature of the absorption spectra of the three GQDs is that the first peak representing the optical gap is of low to moderate intensity, while the intense peaks appear at higher energies. For each GQD, PPP model calculations performed with the screened parameters agree well with the experimental results of the corresponding PAH, and also with the TDDFT calculations.

The development of the internet has allowed for the global distribution of content, redefining media communication and property structures through various streaming platforms. Previous studies successfully clarified the factors contributing to trends in each streaming service, yet the similarities and differences between platforms are commonly unexplored; moreover, the influence of social connections and cultural similarity is usually overlooked. We hereby examine the social aspects of three significant streaming services--Netflix, Spotify, and YouTube--with an emphasis on the dissemination of content across countries. Using two-year-long trending chart datasets, we find that streaming content can be divided into two types: video-oriented (Netflix) and audio-oriented (Spotify). This characteristic is differentiated by accounting for the significance of social connectedness and linguistic similarity: audio-oriented content travels via social links, but video-oriented content tends to spread throughout linguistically akin countries. Interestingly, user-generated contents, YouTube, exhibits a dual characteristic by integrating both visual and auditory characteristics, indicating the platform is evolving into unique medium rather than simply residing a midpoint between video and audio media.

Understanding the dynamic self-assembly mechanisms of carbon nanotube (CNT) forests is necessary to advance their technological promise. Here, in-situ environmental scanning electron microscope (ESEM) chemical vapor deposition (CVD) synthesis observes the real-time nucleation, assembly, delamination, and self-termination of dense ($>$ 10$^9$ CNT/cm$^2$), tall ($>$ 100 $\mu$m) CNT forests in real time. Forest synthesis is continuously observed from nucleation to self-termination. Assembly forces generated near the substrate detach CNTs from the substrate, which simulation suggests requires approximately 10 nN of tensile force. Delamination initiates at both the CNT-catalyst and the catalyst-substrate interfaces, indicating multiple delamination mechanism. Digital image correlation applied to SEM image sequences measures time-invariant strain within growing forests, indicating that forests grow as rigid bodies after liftoff. The Meta CoTracker algorithm measured CNT growth rates reduce from 50 nm/sec to full termination over 150 seconds. This work provides a robust strategy to observe and measure CVD material synthesis in-situ using ESEM. The method is uniquely suited to observe population-based phenomena at both nanometer spatial resolution and at a highly scalable field of view.

3D super-resolution fluorescence microscopy typically requires sophisticated setups, sample preparation, or long measurements. A notable exception, SOFI, only requires recording a sequence of frames and no hardware modifications whatsoever but being a wide-field method, it faces problems in thick, dense samples. We combine SOFI with temporal focusing two-photon excitation -- the wide-field method that is capable of excitation of a thin slice in 3D volume. Both methods are easy to implement in a standard microscope, and by merging them, we obtain super-resolved 3D images of neurons stained with quantum dots. Our approach offers reduced bleaching and an improved signal-to-background ratio that can be used when robust resolution improvement is required in thick, dense samples.

A new combined sub-filter scale turbulence and shock-capturing model is developed for high-order finite volume numerics, extending previous work to unstructured solvers. Block Spectral Stresses (BSS) method relies on the spectra of the velocity gradients to estimate the subfilter scale stresses, heat-flux, and pressure-work based on the resolved field. The method is able to capture shocks with numerical order up to 25 and in a shock-vortex interaction simulation is able to capture the shock and not interfere with the vortex structure. In turbulence calculations the new method is compared with Smagorinsky, dynamic Smagorinsky, and Vreman methods adapted to a block spectral code. In the simulations of homogeneous isotropic turbulence, the new model is worse than the others when on coarse meshes and better on finer ones. Instead, for supersonic and hypersonic channel flow the case is the opposite because as expected the sub-filter terms are mostly depend on the numerical order and not the mesh resolution.

The nitrogen-vacancy (NV) center in diamond is a prime candidate for quantum sensing technologies. Ongoing miniaturization calls for ever-smaller sensors maintaining good measurement performance. Here, we present a fully integrated mechanically robust fiber-based endoscopic sensor capable of $5.9\,\mathrm{nT}/ \sqrt{\mathrm{Hz}}$ magnetic field sensitivity utilizing $15\,\mathrm{\mu m}$ sized microdiamonds at a microwave power of $50\,\mathrm{mW}$ and optical power of $2.15\,\mathrm{mW}$. A direct laser writing process is used to localize a diamond containing NV centers above the fiber's core by a polymer structure. This structure enables stable optical access and independent guiding of excitation and fluorescent light in different optical fibers. This separation strongly reduces the contribution of autofluorescence from the excitation light in the optical fiber. Moreover, a metallic direct laser written antenna structure is created next to the fibers' facet, allowing microwave manipulation of the NV centers' spins. The fabricated endoscopic sensor provides a robust platform with a tip diameter of $1.25\,\mathrm{mm}$. The device enables remote optical and microwave access to perform the full range of coherent spin measurements with NV centers at a spatial resolution of $15\,\mathrm{\mu m}$. We demonstrate the capability of vector magnetic field measurements in a magnetic field as used in state-of-the-art ultracold quantum gas experiments, opening a potential field in which high resolution and high sensitivity are necessary.

A high-performance p-type transparent conductor (TC) does not yet exist, but could lead to advances in a wide range of optoelectronic applications and enable new architectures for, e.g., next-generation photovoltaic (PV) devices. High-throughput computational material screenings have been a promising approach to filter databases and identify new p-type TC candidates, and some of these predictions have been experimentally validated. However, most of these predicted candidates do not have experimentally-achieved properties on par with n-type TCs used in solar cells, and therefore have not yet been used in commercial devices. Thus, there is still a significant divide between transforming predictions into results that are actually achievable in the lab, and an even greater lag in scaling predicted materials into functional devices. In this perspective, we outline some of the major disconnects in this materials discovery process -- from scaling computational predictions into synthesizable crystals and thin films in the laboratory, to scaling lab-grown films into real-world solar devices -- and share insights to inform future strategies for TC discovery and design.

The displacement of a viscous liquid by air in the narrow gap between two parallel plates - a Hele-Shaw channel - is an exemplar of complex pattern formation. Typically, bubbles or fingers of air propagate steadily at low values of the driving parameter. However, as the driving parameter increases, they can exhibit disordered pattern-forming dynamics. In this paper, we demonstrate experimentally that a remote perturbation of the bubble's tip can drive time-periodic bubble propagation: a fundamental building block of complex unsteady dynamics. We exploit the propensity of a group of bubbles to self-organise into a fixed spatial arrangement in a Hele-Shaw channel with a centralised depth-reduction in order to apply a sustained perturbation to a bubble's shape as it propagates. We find that the bubble with a perturbed shape begins to oscillate after the system undergoes a supercritical Hopf bifurcation upon variation of the tip perturbation and dimensionless flow rate. The oscillation cycle features the splitting of the bubble's tip and advection of the resulting finger-like protrusion along the bubble's length until it is absorbed by the bubble's advancing rear. The restoral of the bubble's tip follows naturally because the system is driven by a fixed flow rate and the perturbed bubble is attracted to the weakly unstable, steadily propagating state that is set by the ratio of imposed viscous and capillary forces. Our results suggest a generic mechanism for time-periodic dynamics of propagating curved fronts subject to a steady shape perturbation.

Resistive Plate Chambers (RPCs) are gaseous detectors widely used in high energy physics experiments, operating with a gas mixture primarily containing Tetrafluoroethane (C$_{2}$H$_{2}$F$_{4}$), commonly known as R-134a, which has a global warming potential (GWP) of 1430. To comply with European regulations and explore environmentally friendly alternatives, the RPC EcoGas@GIF++ collaboration, involving ALICE, ATLAS, CMS, LHCb/SHiP, and EP-DT communities, has undertaken intensive R\&D efforts to explore new gas mixtures for RPC technology. A leading alternative under investigation is HFO1234ze, boasting a low GWP of 6 and demonstrating reasonable performance compared to R-134a. Over the past few years, RPC detectors with slightly different characteristics and electronics have been studied using HFO and CO$_{2}$-based gas mixtures at the CERN Gamma Irradiation Facility. An aging test campaign was launched in August 2022, and during the latest test beam in July 2023, all detector systems underwent evaluation. This contribution will report the results of the aging studies and the performance evaluations of the detectors with and without irradiation.

In the 17th and 18th centuries, several natural philosophers studied the phenomenon of refraction and attempted to obtain the Snell law from various assumptions. Lacking experimental data, it was generally believed that light travels faster in a refracting medium than in air. In the present article, I review the contributions to the problem of light refraction by Descartes, Fermat, Huygens, Leibniz, Newton, Clairaut, and finally Maupertuis who established a principle of least action based on his own approach to the problem.

This computational research study analyzes the increase of the specific charge capacity that comes with the reduction of the anisotropic volume expansion during lithium ion insertion within silicon nanowires. This research paper is a continuation from previous work that studied the expansion rate and volume increase. It has been determined that when the lithium ion concentration is decreased by regulating the amount of Li ion flux, the lithium ions to silicon atoms ratio, represented by x, decreases within the amorphous lithiated silicon (a-LixSi) material. This results in a decrease in the volumetric strain of the lithiated silicon nanowire as well as a reduction in Maxwell stress that was calculated and Youngs elastic module that was measured experimentally using nanoindentation. The conclusion as will be seen is that as there is a decrease in lithium ion concentration there is a corresponding decrease in anisotropic volume and a resulting increase in specific charge capacity. In fact the amplification of the electromagnetic field due to the electron flux that created detrimental effects for a fully lithiated silicon nanowire at x = 3.75 which resulted in over a 300 percent volume expansion becomes beneficial with the decrease in lithium ion flux as x approaches 0.75, which leads to a marginal volume increase of 25 percent. This could lead to the use of crystalline silicon, c-Si, as an anode material that has been demonstrated in many previous research works to be ten times greater charge capacity than carbon base anode material for lithium ion batteries.

The multi-reference coupled-cluster Monte Carlo (MR-CCMC) algorithm is a determinant-based quantum Monte Carlo (QMC) algorithm that is conceptually similar to Full Configuration Interaction QMC (FCIQMC). It has been shown to offer a balanced treatment of both static and dynamic correlation while retaining polynomial scaling, although scaling to large systems with significant strong correlation remained impractical. In this paper, we document recent algorithmic advances that enable rapid convergence and a more black-box approach to the multi-reference problem. These include a logarithmically scaling metric-tree based excitation acceptance algorithm to search for determinants connected to the model space at the desired excitation level and a symmetry-screening procedure for the reference space. We show that, for moderately sized model spaces, the new search algorithm brings about an approximately 8-fold acceleration of one MR-CCMC iteration, while the symmetry screening procedure reduces the number of active model space determinants at essentially no loss of accuracy. We also introduce a stochastic implementation of an approximate wall projector, which is the infinite imaginary time limit of the exponential projector, using a truncated expansion of the wall function in Chebyshev polynomials. We show that this wall-Chebyshev projector requires significantly fewer applications of the Hamiltonian to achieve the same statistical convergence. We have applied these methods to calculate the binding curve of the beryllium and carbon dimers with large active spaces, obtaining binding curves that are close to FCIQMC quality. Notably, the wall-Chebyshev projector presented here can be applied to accelerate any projector-based QMC algorithm. This paper represents a significant step in the algorithmic maturation of the MR-CCMC algorithm and paves the way for large-scale calculations.

The paper presents a variational quantum algorithm to solve initial-boundary value problems described by second-order partial differential equations. The approach uses hybrid classical/quantum hardware that is well suited for quantum computers of the current noisy intermediate-scale quantum era. The partial differential equation is initially translated into an optimal control problem with a modular control-to-state operator (ansatz). The objective function and its derivatives required by the optimizer can efficiently be evaluated on a quantum computer by measuring an ancilla qubit, while the optimization procedure employs classical hardware. The focal aspect of the study is the treatment of boundary conditions, which is tailored to the properties of the quantum hardware using a correction technique. For this purpose, the boundary conditions and the discretized terms of the partial differential equation are decomposed into a sequence of unitary operations and subsequently compiled into quantum gates. The accuracy and gate complexity of the approach are assessed for second-order partial differential equations by classically emulating the quantum hardware. The examples include steady and unsteady diffusive transport equations for a scalar property in combination with various Dirichlet, Neumann, or Robin conditions. The results of this flexible approach display a robust behavior and a strong predictive accuracy in combination with a remarkable polylog complexity scaling in the number of qubits of the involved quantum circuits. Remaining challenges refer to adaptive ansatz strategies that speed up the optimization procedure.

Human knowledge is largely implicit and relational -- do we have a friend in common? can I walk from here to there? In this work, we leverage the combinatorial structure of graphs to quantify human priors over such relational data. Our experiments focus on two domains that have been continuously relevant over evolutionary timescales: social interaction and spatial navigation. We find that some features of the inferred priors are remarkably consistent, such as the tendency for sparsity as a function of graph size. Other features are domain-specific, such as the propensity for triadic closure in social interactions. More broadly, our work demonstrates how nonclassical statistical analysis of indirect behavioral experiments can be used to efficiently model latent biases in the data.

The Quantitative Reflectance Imaging System (QRIS) is a laboratory-based spectral imaging system constructed to image the sample of asteroid Bennu delivered to Earth by the Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer (OSIRIS-REx) spacecraft. The system was installed in the OSIRIS-REx cleanroom at NASA's Johnson Space Center to collect data during preliminary examination of the Bennu sample. QRIS uses a 12-bit machine vision camera to measure reflectance over wavelength bands spanning the near ultraviolet to the near infrared. Raw data are processed by a calibration pipeline that generates a series of monochromatic, high-dynamic-range reflectance images, as well as band ratio maps, band depth maps, and 3-channel color images. The purpose of these spectral reflectance data is to help characterize lithologies in the sample and compare them to lithologies observed on Bennu by the OSIRIS-REx spacecraft. This initial assessment of lithological diversity was intended to help select the subsamples that will be used to address mission science questions about the early solar system and the origins of life and to provide important context for the selection of representative subsamples for preservation and distribution to international partners. When QRIS imaged the Bennu sample, unexpected calibration issues arose that had not been evident at imaging rehearsals and negatively impacted the quality of QRIS data. These issues were caused by stray light within the lens and reflections off the glovebox window and interior, and were exacerbated by the sample's extremely low reflectance. QRIS data were useful for confirming conclusions drawn from other data, but reflectance and spectral data from QRIS alone unfortunately have limited utility.

Icy moons with subsurface oceans of liquid water rank among the most promising astrobiological targets in our Solar System. In this work, we assess the feasibility of deploying laser sail technology in precursor life-detection missions. We investigate such laser sail missions to Enceladus and Europa, as these two moons emit plumes that seem accessible to in situ sampling. Our study suggests that GigaWatt laser technology could accelerate a $100$ kg probe to a speed of $\sim{30}\, \mathrm{km\, s^{-1}}$, thereupon reaching Europa on timescales of $1$-$4$ years and Enceladus with flight times of $3$-$6$ years. Although the ideal latitudes for the laser array vary, placing the requisite infrastructure close to either the Antarctic or Arctic Circles might represent technically viable options for an Enceladus mission. Crucially, we determine that the minimum encounter velocities with these moons (about ${6}\,\mathrm{km\,s^{-1}}$) may be near-optimal for detecting biomolecular building blocks (e.g., amino acids) in the plumes by means of a mass spectrometer akin to the Surface Dust Analyzer onboard the \emph{Europa Clipper} mission. In summary, icy moons in the Solar System are potentially well-suited for exploration via the laser sail architecture approach, especially where low encounter speeds and/or multiple missions are desirable.

This work presents an algorithm for determining the parameters of a nonlinear dynamic model of the respiratory system in patients undergoing assisted ventilation. Using the pressure and flow signals measured at the mouth, the model's quadratic pressure-volume characteristic is fit to this data in each respiratory cycle by appropriate estimates of the model parameters. Parameter changes during ventilation can thus also be detected. The algorithm is first refined and assessed using data derived from simulated patients represented through a sigmoidal pressure-volume characteristic with hysteresis. As satisfactory results are achieved with the simulated data, the algorithm is evaluated with real data obtained from actual patients undergoing assisted ventilation. The proposed nonlinear dynamic model and associated parameter estimation algorithm yield closer fits than the static linear models computed by respiratory machines, with only a minor increase in computation. They also provide more information to the physician, such as the pressure-volume (P-V) curvature and the condition of the lung (whether normal, under-inflated, or over-inflated). This information can be used to provide safer ventilation for patients, for instance by ventilating them in the linear region of the respiratory system.

We present a new method to compute the solution to a nonlinear tensor differential equation with dynamical low-rank approximation. The idea of dynamical low-rank approximation is to project the differential equation onto the tangent space of a low-rank tensor manifold at each time. Traditionally, an orthogonal projection onto the tangent space is employed, which is challenging to compute for nonlinear differential equations. We introduce a novel interpolatory projection onto the tangent space that is easily computed for many nonlinear differential equations and satisfies the differential equation at a set of carefully selected indices. To select these indices, we devise a new algorithm based on the discrete empirical interpolation method (DEIM) that parameterizes any tensor train and its tangent space with tensor cross interpolants. We demonstrate the proposed method with applications to tensor differential equations arising from the discretization of partial differential equations.

We study the coherence of two coupled spin qubits in the presence of a bath of nuclear spins simulated using generalized cluster correlation expansion (gCCE) method. In our model, two electron spin qubits coupled with isotropic exchange or magnetic dipolar interactions interact with an environment of random nuclear spins. We study the time-evolution of the two-qubit reduced density matrix (RDM) and resulting decay of the off diagonal elements, corresponding to decoherence, which allows us to calculate gate fidelity in the regime of pure dephasing. We contrast decoherence when the system undergoes free evolution and evolution with dynamical decoupling pulses applied. Moreover, we study the dependence of decoherence on external magnetic field and system parameters which mimic realistic spin qubits, emphasizing magnetic molecules. Lastly, we comment on the application and limitations of gCCE in simulating nuclear-spin induced two-qubit relaxation processes.

A linear sixth-order partial differential equation (PDE) of ``parabolic'' type describes the dynamics of thin liquid films beneath surfaces with elastic bending resistance when deflections from the equilibrium film height are small. On a finite domain, the associated sixth-order Sturm--Liouville eigenvalue value problem is self-adjoint for the boundary conditions corresponding to a thin film in a closed trough, and the eigenfunctions form a complete orthonormal set. Using these eigenfunctions, we derive the Green's function for the governing sixth-order PDE on a finite interval and compare it to the known infinite-line solution. Further, we propose a Galerkin spectral method based on the constructed sixth-order eigenfunctions and their derivative expansions. The system of ordinary differential equations for the time-dependent expansion coefficients is solved by standard numerical methods. The numerical approach is applied to versions of the governing PDE with a second-order derivative (in addition to the sixth-order one), which arises from gravity acting on the film. In the absence of gravity, we demonstrate the self-similar intermediate asymptotics of initially localized disturbances on the film surface, at least until the disturbances ``feel'' the finite boundaries, and show that the derived Green's function is the global attractor for such solutions. In the presence of gravity, we use the proposed spectral numerical method to demonstrate that self-similar behavior persists, albeit for shortened intervals of time, even for large values of the gravity-to-bending ratio.

Dynamics of Langmuir modes - Langmuir waves (LW) and shear Langmuir vortices (SLV) - in kinematically complex astrophysical plasma flows is studied. It is found that they exhibit a number of peculiar, velocity shear induced, asymptotically persistent phenomena: efficient energy exchange with the background flow, various kinds of instabilities, leading to their exponential growth; echoing solutions with persistent wave-vortex-wave conversions. Remarkable similarity of these phenomena with ones happening with compressible acoustic modes, revealed in (Mahajan and Rogava 1999) is pointed out. The relevance and possible importance of these phenomena for different types of astrophysical plasma flow patterns with kinematic complexity is discussed. In particular, we argue that these physical processes may account for the persistent appearance of plasma oscillations in the heliosphere and in interstellar plasma flows. In particular, we believe that kinematically complex motion of plasma may naturally lead to asymptotically persistent appearance of Langmuir modes born, grown, fed, sustained and maintained by these flows.

Pure dephasing and spontaneous emission are two non-unitary processes of atoms or spins interacting with fluctuating electromagnetic (EM) modes. Collective spontaneous emission (e.g., superradiance) originates from interactions with EM modes in resonance with atoms and has received considerable attention. Meanwhile, the analogous collective dephasing phenomena remain poorly understood. Here, we introduce the nano-EM super-dephasing phenomenon arising in the photonic environment near lossy material interfaces. We show that this effect is enhanced by over 10 orders of magnitude compared to free space or photonic cavities due to the presence of long-range correlations in low-frequency evanescent EM fluctuations. We unravel the universality of nano-EM super-dephasing behaviors near ferrimagnets, metals, and superconductors and their dependence on low-frequency material properties. We demonstrate that the scaling of nano-EM super-dephasing is independent of EM modes' wavelengths and differs from the conventional $N^2$ scaling of superradiance by analyzing the decoherence of entangled states, including GHZ states. Finally, we show how to experimentally isolate and control super-dephasing to open interesting frontiers for scalable quantum systems.

De-noising is a prominent step in the spectra post-processing procedure. Previous machine learning-based methods are fast but mostly based on supervised learning and require a training set that may be typically expensive in real experimental measurements. Unsupervised learning-based algorithms are slow and require many iterations to achieve convergence. Here, we bridge this gap by proposing a training-set-free two-stage deep learning method. We show that the fuzzy fixed input in previous methods can be improved by introducing an adaptive prior. Combined with more advanced optimization techniques, our approach can achieve five times acceleration compared to previous work. Theoretically, we study the landscape of a corresponding non-convex linear problem, and our results indicates that this problem has benign geometry for first-order algorithms to converge.

Thermophoresis is an effective method to drive the motion of nanoparticles in fluids. The transport of nanoparticles in polymer networks has significant fundamental and applied importance in biology and medicine, and can be described as Brownian particles crossing entropic barriers. This study proposes a novel extension of dissipative particle dynamics (DPD), called the single-particle energy-conserving dissipative particle dynamics (seDPD), which combines the features of single-particle dissipative particle dynamics (sDPD) and energy-conserving dissipative particle dynamics (eDPD) to simulate the thermophoresis of nanoparticles under temperature gradients. The reliability of the seDPD method is verified by considering the viscosity, thermal diffusivity, and hydrodynamic drag force on the nanoparticles. Using this method, the transport of nanoparticles driven by the thermophoretic force across the polymer network is simulated. The results show that the nanoparticles exhibit the phenomenon of giant acceleration of diffusion (GAD) in the polymer network, indicating that Brownian particles can exhibit GAD when crossing entropic barriers.

Cardiovascular diseases (CVDs) are the leading cause of death worldwide, with blood pressure serving as a crucial indicator. Arterial blood pressure (ABP) waveforms provide continuous pressure measurements throughout the cardiac cycle and offer valuable diagnostic insights. Consequently, there is a significant demand for non-invasive and cuff-less methods to measure ABP waveforms continuously. Accurate prediction of ABP waveforms can also improve the estimation of mean blood pressure, an essential cardiovascular health characteristic. This study proposes a novel framework based on the physics-informed DeepONet approach to predict ABP waveforms. Unlike previous methods, our approach requires the predicted ABP waveforms to satisfy the Navier-Stokes equation with a time-periodic condition and a Windkessel boundary condition. Notably, our framework is the first to predict ABP waveforms continuously, both with location and time, within the part of the artery that is being simulated. Furthermore, our method only requires ground truth data at the outlet boundary and can handle periodic conditions with varying periods. Incorporating the Windkessel boundary condition in our solution allows for generating natural physical reflection waves, which closely resemble measurements observed in real-world cases. Moreover, accurately estimating the hyper-parameters in the Navier-Stokes equation for our simulations poses a significant challenge. To overcome this obstacle, we introduce the concept of meta-learning, enabling the neural networks to learn these parameters during the training process.

Striving for higher gate fidelity is crucial not only for enhancing existing noisy intermediate-scale quantum (NISQ) devices but also for unleashing the potential of fault-tolerant quantum computation through quantum error correction. A recently proposed theoretical scheme, the double-transmon coupler (DTC), aims to achieve both suppressed residual interaction and a fast high-fidelity two-qubit gate simultaneously, particularly for highly detuned qubits. Harnessing the state-of-the-art fabrication techniques and a model-free pulse-optimization process based on reinforcement learning, we translate the theoretical DTC scheme into reality, attaining fidelities of 99.92% for a CZ gate and 99.98% for single-qubit gates. The performance of the DTC scheme demonstrates its potential as a competitive building block for superconducting quantum processors.

The Variational Quantum Eigensolver (VQE) is widely considered to be a promising candidate for a quantum-classical algorithm which could achieve near-term quantum advantage. However, current levels of hardware noise can require extensive application of error-mitigation techniques to achieve reliable computations. In this work, we use several IBM devices to explore a finite-size spin model with multiple `phase-like' regions characterized by distinct ground-state configurations. Using pre-optimized VQE solutions, we demonstrate that in contrast to calculating the energy, where zero-noise extrapolation is required in order to obtain qualitatively accurate yet still unreliable results, calculations of the energy derivative, two-site spin correlation functions, and the fidelity susceptibility yield accurate behavior across multiple regions, even with minimal or no application of error-mitigation approaches. Taken together, these sets of observables could be used to identify level crossings in VQE solutions in a simple and noise-robust manner, with potential near-term application to identifying quantum phase transitions, avoided crossings and non-adiabatic conical intersections in electronic structure calculations.

Fractional killing in response to drugs is a hallmark of non-genetic cellular heterogeneity. Yet how individual lineages evade drug treatment, as observed in bacteria and cancer cells, is not quantitatively understood. We analyse a stochastic population model with age-dependent division and death rates and characterise the emergence of fractional killing as a stochastic phenomenon under constant and periodic drug environments. In constant environments, increasing cell cycle noise induces a phase transition from complete to fractional killing, while increasing death noise can induce the reverse transition. In periodic drug environments, we discover survival resonance phenomena that give rise to peaks in the survival probabilities at division or death times that are multiples of the environment duration not seen in unstructured populations.

The hitherto unknown author of a citation by Goethe in his History of Colours is identified as J. E. Montucla and the context of Montucla's quotation is discussed.

Atomic superfluids formed using Bose-Einstein condensates (BECs) in a ring trap are currently being investigated in the context of superfluid hydrodynamics, quantum sensing and matter-wave interferometry. The characterization of the rotational properties of such superfluids is important, but can presently only be performed by using optical absorption imaging, which completely destroys the condensate. Recent studies have proposed coupling the ring BEC to optical cavity modes carrying orbital angular momentum to make minimally destructive measurements of the condensate rotation. The sensitivity of these proposals, however, is bounded below by the standard quantum limit set by the combination of laser shot noise and radiation pressure noise. In this work, we provide a theoretical framework that exploits the fact that the interaction between the scattered modes of the condensate and the light reduces to effective optomechanical equations of motion. We present a detailed theoretical analysis to demonstrate that the use of squeezed light and backaction evasion techniques allows the angular momentum of the condensate to be sensed with noise well below the standard quantum limit. Our proposal is relevant to atomtronics, quantum sensing and quantum information.

Polymer translocation in crowded environments is a ubiquitous phenomenon in biological systems. We studied polymer translocation through a pore in free, one-sided (asymmetric), and two-sided (symmetric) crowded environments. Extensive Langevin dynamics simulation is employed to model the dynamics of the flexible polymer and crowding particles. We studied how crowding size and packing fraction play a crucial role in the translocation process. After determining the standard scaling properties of the translocation probability, time, and MSD, we observed that the translocation rate and bead velocities are location-dependent as we move along the polymer backbone, even in a crowd-free environment. Counter-intuitively, translocation rate and bead velocities showed the opposite behavior; for example, middle monomers near the pore exhibit maximum bead velocity and minimum translocation rate. Free energy calculation for asymmetrically placed polymer indicates there exists a critical number of segments that make the polymer to prefer the receiver side for translocation. For one-sided crowding, we have identified a critical crowding size above which there exists a non-zero probability to translocate into the crowding side instead of the free side. Moreover, we have observed that shifting the polymer towards the crowded side compensates for one-sided crowding, yielding an equal probability akin to a crowder-free system. Under two-sided crowding, the mechanism of how a slight variation in crowder size and packing fraction can force a polymer to switch its translocation direction is proposed, which has not been explored before. Using this control we achieved an equal translocation probability like a crowd-free scenario. These conspicuous yet counter-intuitive phenomena are rationalized by simple theoretical arguments based on osmotic pressure and radial entropic forces.

We design and implement quantum circuits for the simulation of the one-dimensional wave equation on the Quantinuum H1-1 quantum computer. The circuit depth of our approach scales as $O(n^{2})$ for $n$ qubits representing the solution on $2^n$ grid points, and leads to infidelities of $O(2^{-4n} t^{2})$ for simulation time $t$ assuming smooth initial conditions. By varying the qubit count we study the interplay between the algorithmic and physical gate errors to identify the optimal working point of minimum total error. Our approach to simulating the wave equation can readily be adapted to other quantum processors and serve as an application-oriented benchmark.

Convective processes are crucial in shaping exoplanetary atmospheres but are computationally expensive to simulate directly. A novel technique of simulating moist convection on tidally locked exoplanets is to use a global 3D model with a stretched mesh. This allows us to locally refine the model resolution to 4.7 km and resolve fine-scale convective processes without relying on parameterizations. We explore the impact of mesh stretching on the climate of a slowly rotating TRAPPIST-1e-like planet, assuming it is 1:1 tidally locked. In the stretched-mesh simulation with explicit convection, the climate is 5 K colder and 25% drier than that in the simulations with parameterized convection. This is due to the increased cloud reflectivity and exacerbated by the diminished greenhouse effect due to less water vapor. At the same time, our stretched-mesh simulations reproduce the key characteristics of the global climate of tidally locked rocky exoplanets, without any noticeable numerical artifacts. Our methodology opens an exciting and computationally feasible avenue for improving our understanding of 3D mixing in exoplanetary atmospheres. Our study also demonstrates the feasibility of a global stretched mesh configuration for LFRic-Atmosphere, the next-generation Met Office climate and weather model.

Vesicles are important surrogate structures made up of multiple phospholipids and cholesterol distributed in the form of a lipid bilayer. Tubular vesicles can undergo pearling i.e., formation of beads on the liquid thread akin to the Rayleigh-Plateau instability. Previous studies have inspected the effects of surface tension on the pearling instabilities of single-component vesicles. In this study, we perform a linear stability analysis on a multicomponent cylindrical vesicle. We solve the Stokes equations along with the Cahn-Hilliard equations to develop the linearized dynamic equations governing the vesicle shape and surface concentration fields. This helps us show that multicomponent vesicles can undergo pearling, buckling, and wrinkling even in the absence of surface tension, which is a significantly different result from studies on single-component vesicles. This behaviour arises due to the competition between the free energies of phase separation, line tension, and bending for this multi-phospholipid system. We determine the conditions under which axisymmetric and non-axisymmetric modes are dominant, and supplement our results with an energy analysis that shows the sources for these instabilities. We further show that these trends qualitatively match recent experiments.

Spin parity effects refer to those special situations where a dichotomy in the physical behavior of a system arises, solely depending on whether the relevant spin quantum number is integral or half-odd integral. As is the case with the Haldane conjecture in antiferromagnetic spin chains, their pursuit often provides deep insights and invokes new developments in quantum condensed matter physics. Here we put forth a simple and general scheme for generating such effects in any spatial dimension through the use of anisotropic interactions, a setup within reasonable reach of state-of-the-art cold-atom implementations. We demonstrate its utility through a detailed analysis of the magnetization behavior of a specific one-dimensional spin chain model -- an anisotropic antiferromagnet in a transverse magnetic field, unraveling along the way the quantum origin of finite-size effects observed in the magnetization curve that had previously been noted but not clearly understood.

Nonlinear quantum photonics serves as a cornerstone in photonic quantum technologies, such as universal quantum computing and quantum communications. The emergence of integrated photonics platform not only offers the advantage of large-scale manufacturing but also provides a variety of engineering methods. Given the complexity of integrated photonics engineering, a comprehensive simulation framework is essential to fully harness the potential of the platform. In this context, we introduce a nonlinear quantum photonics simulation framework which can accurately model a variety of features such as adiabatic waveguide, material anisotropy, linear optics components, photon losses, and detectors. Furthermore, utilizing the framework, we have developed a device scheme, chip-scale temporal walk-off compensation, that is useful for various quantum information processing tasks. Applying the simulation framework, we show that the proposed device scheme can enhance the squeezing parameter of photon-pair sources and the conversion efficiency of quantum frequency converters without relying on higher pump power.

ALICE (A Large Ion Collider Experiment) studies the Quark-Gluon Plasma (QGP): a deconfined state of matter obtained in ultra-relativistic heavy-ion collisions. One of the probes for QGP study are quarkonia and open heavy flavour, of which ALICE exploits the muonic decay. A set of Resistive Plate Chambers (RPCs), placed in the forward rapidity region of the ALICE detector, is used for muon identification purposes. The correct operation of these detectors is ensured by the choice of the proper gas mixture. Currently they are operated with a mixture of C$_{2}$H$_{2}$F$_{4}$, i-C$_{4}$H$_{10}$ and SF$_{6}$ but, starting from 2017, new EU regulations have enforced a progressive phase-out of C$_{2}$H$_{2}$F$_{4}$ because of its large Global Warming Potential (GWP), making it difficult and costly to purchase. CERN asked LHC experiments to reduce greenhouse gases emissions, to which RPC operation contributes significantly. A possible candidate for C$_{2}$H$_{2}$F$_{4}$ replacement is the C$_{3}$H$_{2}$F$_{4}$ (diluted with other gases, such as CO$_{2}$), which has been extensively tested using cosmic rays. Promising gas mixtures have been devised; the next crucial steps are the detailed in-beam characterization of such mixtures as well as the study of their performance under increasing irradiation levels. This contribution will describe the methodology and results of beam tests carried out at the CERN GIF++ (equipped with a high activity $^{137}$Cs source and muon beam) with an ALICE-like RPC prototype, operated with several mixtures with varying proportions of CO$_{2}$, C$_{3}$H$_{2}$F$_{4}$, i-C$_{4}$H$_{10}$ and SF$_{6}$ . Absorbed currents, efficiencies, prompt charges, cluster sizes, time resolutions and rate capabilities will be presented, both from digitized (for detailed shape and charge analysis) and discriminated (using the same front-end electronics as employed in ALICE) signals.

Quantum simulation using synthetic quantum systems offers unique opportunities to explore open questions in many-body physics and a path for the generation of useful entangled states. Nevertheless, so far many quantum simulators have been fundamentally limited in the models they can mimic. Here, we are able to realize an all-to-all interaction with arbitrary quadratic Hamiltonian or effectively an infinite range tunable Heisenberg XYZ model. This is accomplished by engineering cavity-mediated four-photon interactions between 700 rubidium atoms in which we harness a pair of momentum states as the effective pseudo spin or qubit degree of freedom. Using this capability we realize for the first time the so-called two-axis counter-twisting model, an iconic XYZ collective spin model that can generate spin-squeezed states that saturate the Heisenberg limit bound. The versatility of our platform to include more than two relevant momentum states, combined with the flexibility of the simulated Hamiltonians by adding cavity tones opens rich opportunities for quantum simulation and quantum sensing in matter-wave interferometers and other quantum sensors such as optical clocks and magnetometers.

We present a natural language processing pipeline that was used to extract polymer solar cell property data from the literature and simulate various active learning strategies. While data-driven methods have been well established to discover novel materials faster than Edisonian trial-and-error approaches, their benefits have not been quantified. Our approach demonstrates a potential reduction in discovery time by approximately 75 %, equivalent to a 15 year acceleration in material innovation. Our pipeline enables us to extract data from more than 3300 papers which is ~5 times larger than similar data sets reported by others. We also trained machine learning models to predict the power conversion efficiency and used our model to identify promising donor-acceptor combinations that are as yet unreported. We thus demonstrate a workflow that goes from published literature to extracted material property data which in turn is used to obtain data-driven insights. Our insights include active learning strategies that can simultaneously optimize the material system and train strong predictive models of material properties. This work provides a valuable framework for research in material science.