This study presents a novel and ecofriendly method for synthesizing ammonium nitrate using activated prepared through air and ammonia plasma treatments. Initially, PAW containing nitrate ions is produced by treating water with air plasma. This PAW air is then frozen and exposed to low pressure NH$_3$ plasma, introducing ammonium ions to from NH$_4$NO$_3$. We systematically investigate the voltage current characteristics of the air and NH$_3$ plasma, analyze the generated species and radicals to understand the mechanism of NH$_4$NO$_3$ formation, and evaluate the effects of process parameters such as NH$_3$ gas pressure, applied voltage, and treatment time on the properties of PAW. Our results indicate that all examined process parameters positively influence the properties of PAW. Among these parameters, the duration of NH$_3$ plasma treatment of PAW ice exerts the most significant effect. Specifically, the concentration of NH4 ions increased by 134.2 percent when the NH$_3$ treatment time was extended from 0.5 h to 1 h, compared to 12.7 and 33.3 percent increases for NH$_3$ pressure, ranging from 0.25 to 0.55 mbar, and applied voltage, ranging from 500 to 700 V, respectively. Similarly, variations in pH, oxidation reduction potential, and electrical conductivity were substantially higher with increased treatment time than with changes in gas pressure and applied voltage. The PAW exhibited a neutral to slightly basic pH, making it ideal for soil applications, thereby addressing the existing issue of the high acidity of PAW and its use in agriculture.

Surface-active agents (surfactants) release potential energy as they migrate from one of two adjacent fluids onto their fluid-fluid interface, a process that profoundly impacts the system's energy and entropy householding. The continuum thermodynamics underlying such a surfactant-enriched binary-fluid system has not yet been explored comprehensively. In this article, we present a mathematical description of such a system, in terms of balance laws, equations of state, and permissible constitutive relations and interface conditions, that satisfies the first and second law of thermodynamics. The interface conditions and permissible constitutive relations are revealed through a Coleman-Noll analysis. We characterize the system's equilibrium by defining equilibrium equivalences and study an example system. With our work, we aim to provide a systematically derived framework that combines and links various elements of existing literature, and that can serve as a thermodynamically consistent foundation for the (numerical) modeling of full surfactant-enriched binary-fluid systems.

We present a new numerical model for solving the Chew-Goldberger-Low system of equations describing a bi-Maxwellian plasma in a magnetic field. Heliospheric and geospace environments are often observed to be in an anisotropic state with distinctly different parallel and perpendicular pressure components. The CGL system represents the simplest leading order correction to the common isotropic MHD model that still allows to incorporate the latter's most desirable features. However, the CGL system presents several numerical challenges: the system is not in conservation form, the source terms are stiff, and unlike MHD it is prone to a loss of hyperbolicity if the parallel and perpendicular pressures become too different. The usual cure is to bring the parallel and perpendicular pressures closer to one another; but that has usually been done in an ad hoc manner. We present a physics-informed method of pressure relaxation based on the idea of pitch-angle scattering that keeps the numerical system hyperbolic and naturally leads to zero anisotropy in the limit of very large plasma beta. Numerical codes based on the CGL equations can, therefore, be made to function robustly for any magnetic field strength, including the limit where the magnetic field approaches zero. The capabilities of our new algorithm are demonstrated using several stringent test problems that provide a comparison of the CGL equations in the weakly and strongly collisional limits. This includes a test problem that mimics interaction of a shock with a magnetospheric environment in 2D.

We demonstrated the analogy between Economics and Gauge Theory of Plasticity and used it to describe the relationship between money supply and inflation at the economic market. The received equations of economical dynamics in phase space are similar to the plasticity equations and economic variables - choice, competition and profit correspond to the state of the market with inflation. We described the meaning of equations and the role of its variables in the stabilization mechanism of the market with inflation. The equation of market equilibrium including the Profit turnover, time changes of competition, capital and choice was discussed in detail.

Inflow control is essential for most fluid mechanics experiments. Although vertically falling soap film flows have been extensively used in the last four decades to study two-dimensional flows, its inflow stability has not yet been discussed in detail. In this article, aiming to improve the inflow stability of the system, we discuss how flow driving systems dominate the inflow states and the statistics of soap film flow properties. We report experimental measurements of inflow rates using different flow driving methods, followed by soap film velocity measurements by Laser Doppler Velocimetry (LDV). The widely-used method of a constant-pressure-head reservoir exhibits a continuous drop of inflow rate. In addition, even when the flow is not disturbed, the soap film displays high magnitudes of velocity fluctuations. The mean square of velocity fluctuation was measured at 4.6% of the mean. We also test other methods without the reservoir, where the flow is directly controlled by a pump; two staggered syringe pumps are able to maintain inflow stability over days and reduce the velocity fluctuation to 0.5% of the mean. From these measurements, we conclude that the drop of inflow rate might be caused by micro/milli-scale air bubbles, which cannot be completely removed in the system. A method to control the inflow actively is necessary in a soap film setup, when a stable and long standing film flow is needed.

Seismic images often contain both coherent and random artifacts which complicate their interpretation. To mitigate these artifacts, we introduce a novel unsupervised deep-learning method based on Deep Image Prior (DIP) which uses convolutional neural networks. Our approach optimizes the network weights to refine the migration velocity model, rather than the seismic image, effectively isolating meaningful image features from noise and artifacts. We apply this method to synthetic and real seismic data, demonstrating significant improvements over standard DIP techniques with minimal computational overhead.

In this work, we provide a mathematical formulation for error propagation in flow trajectory prediction using data-driven turbulence closure modeling. Under the assumption that the predicted state of a large eddy simulation prediction must be close to that of a subsampled direct numerical simulation, we retrieve an upper bound for the prediction error when utilizing a data-driven closure model. We also demonstrate that this error is significantly affected by the time step size and the Jacobian which play a role in amplifying the initial one-step error made by using the closure. Our analysis also shows that the error propagates exponentially with rollout time and the upper bound of the system Jacobian which is itself influenced by the Jacobian of the closure formulation. These findings could enable the development of new regularization techniques for ML models based on the identified error-bound terms, improving their robustness and reducing error propagation.

The tubulin-like protein FtsZ is crucial for cytokinesis in bacteria and many archaea, forming a ring-shaped structure called the Z-ring at the site of cell division. Despite extensive research, the formation of Z-rings is not entirely understood. We propose a theoretical model based on FtsZ's known filament structures, treating them as semiflexible polymers with specific mechanical properties and lateral inter-segment attraction that can stabilize ring formations. Our Langevin dynamics simulations reveal various morphological phases, including open helices, chains, rings, and globules, capturing experimental observations in the fission yeast model using FtsZ from different bacterial species or mutants of Escherichia coli. Using the theoretical model, we explore how treadmilling activity affects Z-ring stability and identify a spooling mechanism of ring formation. The active ring produces contractile, shear, and rotational stresses, which intensify before the Z-ring transitions to an open helix at high activity.

A recent experimental study reports on measuring the temporal duration and the spatial extent of failed attempts to cross an activation barrier (i.e., "loops") for a folding transition in a single molecule and for a Brownian particle trapped within a bistable potential. Within the model of diffusive dynamics, however, both of these quantities are, on the average, exactly zero because of the recrossings of the barrier region boundary. That is, an observer endowed with infinite spatial and temporal resolution would find that finite loops do not exist (or, more precisely, form a set of measure zero). Here we develop a description of the experiment that takes finite experimental resolution into account and show how the experimental uncertainty of localizing the point, in time and space, where the barrier is crossed leads to observable distributions of loop times and sizes. Although these distributions generally depend on the experimental resolution, this dependence, in certain cases, may amount to a simple resolution-dependent factor and thus the experiments do probe inherent properties of barrier crossing dynamics.

This study presents novel active-flow-control (AFC) strategies aimed at achieving drag reduction for a three-dimensional cylinder immersed in a flow at a Reynolds number based on freestream velocity and cylinder diameter of (Re_D=3900). The cylinder in this subcritical flow regime has been extensively studied in the literature and is considered a classic case of turbulent flow arising from a bluff body. The strategies presented are explored through the use of deep reinforcement learning. The cylinder is equipped with 10 independent zero-net-mass-flux jet pairs, distributed on the top and bottom surfaces, which define the AFC setup. The method is based on the coupling between a computational-fluid-dynamics solver and a multi-agent reinforcement-learning (MARL) framework using the proximal-policy-optimization algorithm. Thanks to the acceleration in training facilitated by exploiting the local invariants with MARL, a drag reduction of (8\%) was achieved, with a mass cost efficiency two orders of magnitude lower than those of the existing classical controls in the literature. This development represents a significant advancement in active flow control, particularly in turbulent regimes critical to industrial applications.

This work investigates the unsteady behavior of unstart phenomena within a scramjet inlet using advanced computational techniques. Scramjets and ramjets, with their reliance on inlet compression, offer promising airbreathing propulsion for hypersonic regimes. This research focuses on understanding and modeling the onset of unstart phenomena in supersonic inlets, a critical step towards developing mitigation strategies. These strategies have the potential to improve engine efficiency, range, and maneuverability of hypersonic vehicles. To achieve this, the state-of-the-art compressible flow solver, Eilmer, is used to simulate shockwave behavior within the inlet/isolator of a planar scramjet characterized experimentally at North Carolina State University (NCSU). Baseline comparisons are presented with the wind tunnel experiments via the shock structures present within the isolator section conducted at Mach 3.9 on a 3D scramjet inlet model. Simulations are then carried out at varying angles of attack (0 to 10 deg) and multiple pitch rates (10 deg/sec and 100 deg/sec) to demonstrate the shock train inertial response and to characterize unstart onset. In both cases the timing of inlet unstart is observed to correlate well with the rapid surge in exit pressure as well as shock detachment at the lower leading edge region. Lastly, exit pressures are significantly higher in the 10 deg/s case than in that of the 100 deg/s case at the same angle of attack. These observations suggest that unstart is not only dependent on angle of attack but also on AoA pitch rate. The findings provide valuable insights into the unsteady flow behavior during hypersonic inlet unstart, with potential applications for unstart detection at high angles of attack.

Dispersion management of few-cycle pulses is crucial for ultrafast optics and photonics. Often, nontrivial dispersion is compensated using complex optical systems or minimized through careful design of waveguides. Here, we present dispersion-managed adiabatic frequency conversion enabling efficient downconversion of an 11.1-fs near-IR pulse to an 11.6-fs mid-IR pulse spanning an octave of bandwidth from 2-4 $\mu$m. The adiabatic frequency converter is designed to impart a constant group delay over the entire bandwidth, eliminating the need for complex dispersion management and opening a new avenue for dispersion engineering in ultrafast optics and photonics. Notably, dispersion engineering through the position-dependent conversion position of an adiabatic conversion device constitutes an additional mechanism for dispersion control in ultrafast optics and photonics while maintaining high conversion efficiency for broadband pulses.

This paper presents an extensive characterization of the low-frequency noise (LFN) at room temperature (RT) and cryogenic temperature (4.2 K) of 40-nm bulk-CMOS transistors. The noise is measured over a wide range of bias conditions and geometries to generate a comprehensive overview of LFN in this technology. While the RT results are in-line with the literature and the foundry models, the cryogenic behavior diverges in many aspects. These deviations include changes with respect to RT in magnitude and bias dependence that are conditional on transistor type and geometry, and even an additional systematic Lorentzian feature that is common among individual devices. Furthermore, we find the scaling of the average LFN with the area and its variability to be similar between RT and 4.2 K, with the cryogenic scaling reported systematically for the first time. The findings suggest that, as no consistent decrease of LFN at lower temperatures is observed while the white noise is reduced, the impact of LFN for precision analog design at cryogenic temperatures gains a more predominant role.

The ability to detect and image short-wave infrared light has important applications in surveillance, autonomous navigation, and biological imaging. However, the current infrared imaging technologies often pose challenges due to their large footprints, large thermal noise, and the inability to augment infrared and visible imaging. Here, we demonstrate infrared imaging by nonlinear up conversion to the visible on an ultra-compact, high-quality lithium niobate resonant metasurface. Images with high conversion efficiency and resolution quality are obtained despite the strong nonlocality of the metasurface. We further show the possibility of edge-detection image processing augmented with direct-up conversion imaging for advanced night vision applications.

The surface of Saturn's moon Titan is coated with small molecule organic solids termed cryominerals. Cryominerals play an analogous role to minerals on Earth in Titan's surface geology and geochemistry. To develop a predictive understanding of Titan's surface geochemistry, we need to characterize the structure and dynamics of cryominerals at the molecular scale. We use ab initio molecular dynamics simulations to quantify the structure and dynamics of the acetonitrile:acetylene (1:2) co-crystal at Titan surface conditions. We suggest that acetonitrile:acetylene is in a plastic phase, in which acetonitrile molecules are dynamically disordered about the N-C-C axis on sub-picosecond timescales, and that this rotational, plastic disorder persists at least to temperatures of 30 K. We anticipate that many cryominerals may have plastic phases at or near Titan surface conditions, and understanding this disorder will be crucial to predicting chemistry on Titan's surface.

While much about Alfven eigenmode (AE) stability has been explored in previous and current tokamaks, open questions remain for future burning plasma experiments, especially regarding exact stability threshold conditions and related isotope effects; the latter, of course, requiring good knowledge of the plasma ion composition. In the JET tokamak, eight in-vessel antennas actively excite stable AEs, from which their frequencies, toroidal mode numbers, and net damping rates are assessed. The effective ion mass can also be inferred using measurements of the plasma density and magnetic geometry. Thousands of AE stability measurements have been collected by the Alfven Eigenmode Active Diagnostic in hundreds of JET plasmas during the recent Hydrogen, Deuterium, Tritium, DT, and Helium-4 campaigns. In this novel AE stability database, spanning all four main ion species, damping is observed to decrease with increasing Hydrogenic mass, but increase for Helium, a trend consistent with radiative damping as the dominant damping mechanism. These data are important for confident predictions of AE stability in both non-nuclear (H/He) and nuclear (D/T) operations in future devices. In particular, if radiative damping plays a significant role in overall stability, some AEs could be more easily destabilized in D/T plasmas than their H/He reference pulses, even before considering fast ion and alpha particle drive. Active MHD spectroscopy is also employed on select HD, HT, and DT plasmas to infer the effective ion mass, thereby closing the loop on isotope analysis and demonstrating a complementary method to typical diagnosis of the isotope ratio.

We present a framework for analyzing plasma flow in a rotating mirror. By making a series of physical assumptions, we reduce the magnetohydrodynamic (MHD) equations in a three-dimensional cylindrical system to a one-dimensional system in a shallow, cuboidal channel within a transverse magnetic field, similar to the Hartmann flow in the ducts. We then solve the system both numerically and analytically for a range of values of the Hartmann number and calculate the dependence of the plasma flow speed on the thickness of the insulating end cap. We observe that the mean flow overshoots and decelerates before achieving a steady-state value, a phenomenon that the analytical model cannot capture. This overshoot is directly proportional to the thickness of the insulating end cap and the external electric field, with a weak dependence on the external magnetic field. Our simplified model can act as a benchmark for future simulations of the supersonic mirror device Compact Magnetic Fusion Experiment (CMFX), which will employ more sophisticated physics and realistic magnetic field geometries.

Low-intensity light beams carrying Orbital Angular Momentum (OAM), commonly known as vortex beams, have garnered significant attention due to promising applications in areas ranging from optical trapping to communication. In recent years, there has been a surge in global research exploring the potential of high-intensity vortex laser beams and specifically their interactions with plasmas. This paper provides a comprehensive review of recent advances in this area. Compared to conventional laser beams, intense vortex beams exhibit unique properties such as twisted phase fronts, OAM delivery, hollow intensity distribution, and spatially isolated longitudinal fields. These distinct characteristics give rise to a multitude of rich phenomena, profoundly influencing laser-plasma interactions and offering diverse applications. The paper also discusses future prospects and identifies promising general research areas involving vortex beams. These areas include low-divergence particle acceleration, instability suppression, high-energy photon delivery with OAM, and the generation of strong magnetic fields. With growing scientific interest and application potential, the study of intense vortex lasers is poised for rapid development in the coming years.

As a heavy molecule, barium monofluoride (BaF) presents itself as a promising candidate for measuring permanent electric dipole moment. The precision of such measurements can be significantly enhanced by utilizing a cold molecular sample. Here we report the realization of three-dimensional magneto-optical trapping (MOT) of BaF molecules. Through the repumping of all the vibrational states up to $v=3$, and rotational states up to $N=3$, we effectively close the transition to a leakage level lower than $10^{-5}$. This approach enables molecules to scatter a sufficient number of photons required for laser cooling and trapping. By employing a technique that involves chirping the slowing laser frequency, BaF molecules are decelerated to near-zero velocity, resulting in the capture of approximately $3\times 10^3$ molecules in a dual-frequency MOT setup. Our findings represent a significant step towards the realization of ultracold BaF molecules and the conduct of precision measurements with cold molecules.

A beam profile monitor (BPM) has been developed to measure photon beams at the BM4 beamline of the Mikamine site, Research Center for Accelerator and Radioisotope Science (RARIS-Mikamine; previously known as ELPH) at Tohoku University. The BPM comprises plastic scintillation fibers and SiPMs, enabling high-precision, real-time measurements of photon beams in the 1 GeV region. Data acquisition utilized streaming TDC, a firmware commonly employed in the J-PARC Hadron-hall, enabling real-time detection of high-intensity photon beams with count rates reaching several tens of MHz. With sufficient statistical data, the BPM achieved a 1 s beam-profiling accuracy of 10 {\mu}m. The proposed BPM serves as a valuable resource for future physics experiments at the BM4 photon beamline and will contribute significantly to ongoing accelerator research endeavors.

We introduce a novel laser-scanning optical microscopy technique that employs optical-frequency-comb (OFC) lasers. This method facilitates multimodal spectroscopic imaging by analyzing interferograms produced via a dual-comb spectroscopic approach. Such interferograms capture comprehensive light information, including amplitude, phase, polarization, frequency, and time of flight information, enabling multimodal imaging from a single measurement. We demonstrate the potential of this technique across several spectroscopic imaging applications.

The buoyancy-driven motion of a deformable bubble rising near a vertical hydrophilic wall is studied numerically. We focus on moderately inertial regimes in which the bubble undergoes low-to-moderate deformations and would rise in a straight line in the absence of the wall. Three different types of near-wall motion are observed, depending on the buoyancy-to-viscous and buoyancy-to-capillary force ratios defining the Galilei ($Ga$) and Bond ($Bo$) numbers of the system, respectively. For low enough $Ga$ or large enough $Bo$, bubbles consistently migrate away from the wall. Conversely, for large enough $Ga$ and low enough $Bo$, they perform periodic near-wall bounces. At intermediate $Ga$ and $Bo$, they are first attracted to the wall down to a certain critical distance, and then perform bounces with a decreasing amplitude before stabilizing at this critical separation. Periodic bounces are accompanied by the shedding of a pair of streamwise vortices in the wake, the formation of which is governed by the near-wall shear resulting from the no-slip condition. These vortices provide a repulsive force that overcomes the viscous resistance of the fluid to the departing motion, making the bubble capable of returning to the region where it is attracted again to the wall. Although periodic, the shedding/regeneration cycle of these vortices is highly asymmetric with respect to the lateral bubble displacements, vortices being shed when the gap left between the bubble and the wall reaches its maximum, and reborn only when this gap comes back to its minimum.

This paper explores the friction forces encountered by droplets on non-wetting surfaces, specifically focusing on superhydrophobic and superheated substrates. Employing a combination of experimental techniques, including inclined plane tests and cantilever force sensor measurements, we quantify friction forces across a broad range of velocities and surface types. Our results demonstrate that friction forces vary significantly with changes in droplet velocity and surface characteristics, transitioning from contact line pinning to viscous dissipation in the bulk of the droplet. We propose a universal scaling law that accounts for contact angle hysteresis, viscous dissipation, and aerodynamic drag, providing a comprehensive framework for understanding droplet dynamics on non-wetting surfaces. These findings offer valuable insights for optimizing surface designs in fluid transport and microfluidic applications, paving the way for enhanced efficiency and innovation in these technologies.

Strongly laser-driven semiconductor crystals offer substantial advantages for the study of many-body physics and ultrafast optoelectronics via the high harmonic generation process. While this phenomenon has been employed to investigate the dynamics of solids in the presence of strong laser fields, its potential to be utilized as an attosecond light source has remained unexploited. Here, we demonstrate that the high harmonics generated through the interaction of mid--infrared pulses with a ZnO crystal leads to the production of attosecond pulses, that can be used to trace the ultrafast ionization dynamics of alkali metals. In a cross--correlation approach, we photoionize Cesium atoms with the vacuum-ultraviolet (VUV) high-harmonics in the presence of a mid-infrared laser field. We observe strong oscillations of the photoelectron yield originating from the instantaneous polarization of the atoms by the laser field. The phase of the oscillations encodes the attosecond synchronization of the ionizing high-harmonics and is used for attosecond pulse metrology. This light source opens a new spectral window for attosecond spectroscopy, paving the way for studies of systems with low ionization potentials including neutral atoms, molecules and solids. Additionally, our results highlight the significance of the source for generating non--classical massively entangled light states in the visible--VUV spectral region.

We present a compact, reliable, and robust free-running all-polarization-maintaining erbium (Er) single-cavity dual-comb laser generated via polarization multiplexing with gain sharing. Polarization multiplexing exploits the fast and slow axes of the fiber, while modelocking is achieved through a nonlinear amplifying loop mirror scheme using readily available components. The laser operates at a repetition rate of around 74.74 MHz with a tuning capability in the difference in repetition rates from 500 Hz to 200 kHz. This tunability makes the system more flexible for dual-comb spectroscopy experiments. Consequently, using this laser, we demonstrated a proof-of-principle dual-comb spectroscopy of carbon monoxide (CO), operating without any active stabilization.

The CYGNO collaboration is developing next generation directional Dark Matter (DM) detection experiments, using gaseous Time Projection Chambers (TPCs), as a robust method for identifying Weakly Interacting Massive Particles (WIMPs) below the Neutrino Fog. SF6 is potentially ideal for this since it provides a high fluorine content, enhancing sensitivity to spin-dependent interactions and, as a Negative Ion Drift (NID) gas, reduces charge diffusion leading to improved positional resolution. CF4, although not a NID gas, has also been identified as a favourable gas target as it provides a scintillation signal which can be used for a complimentary light/charge readout approach. These gases can operate at low pressures to elongate Nuclear Recoil (NR) tracks and facilitate directional measurements. In principle, He could be added to low pressure SF6/CF4 without significant detriment to the length of 16S, 12C, and 19F recoils. This would improve the target mass, sensitivity to lower WIMP masses, and offer the possibility of atmospheric operation; potentially reducing the cost of a containment vessel. In this article, we present gas gain and energy resolution measurements, taken with a Multi-Mesh Thick Gaseous Electron Multiplier (MMThGEM), in low pressure SF6 and CF4:SF6 mixtures following the addition of He. We find that the CF4:SF6:He mixtures tested were able to produce gas gains on the order of 10^4 up to a total pressure of 100 Torr. These results demonstrate an order of magnitude improvement in charge amplification in NID gas mixtures with a He component.

Biophotonic nanostructures in butterfly wing scales remain fascinating examples of biological functional materials, with intriguing open questions in regards to formation and evolutionary function. One particularly interesting butterfly species, Erora opisena (Lycaenidae: Theclinae), develops wing scales that contain three-dimensional photonic crystals that closely resemble a single gyroid geometry. Unlike most other gyroid forming butterflies, E. opisena develops discrete gyroid crystallites with a pronounced size gradient hinting at a developmental sequence frozen in time. Here, we use a hyperspectral (wavelength-resolved) microscopy technique to investigate the ultrastructural organisation of these gyroid crystallites in dry, adult wing scales. We show that reflectance corresponds to crystallite size, where larger crystallites reflect green wavelengths more intensely; this relationship could be used to infer size from the optical signal. We further successfully resolve the red-shifted reflectance signal from wing scales immersed in refractive index oils with varying refractive index, including values similar to water or cytosol. Such photonic crystals with lower refractive index contrast may be similar to the hypothesized nanostructural forms in the developing butterfly scales. The ability to resolve these fainter signals hints at the potential of this facile light microscopy method for in vivo analysis of nanostructure formation in developing butterflies.

We review the construction and evolution of mathematical models of the Arabidopsis circadian clock, structuring the discussion into two distinct historical phases of modeling strategies: extension and reduction. The extension phase explores the bottom-up assembly of regulatory networks introducing as many components and interactions as possible in order to capture the oscillatory nature of the clock. The reduction phase deals with functional decomposition, distilling complex models to their essential dynamical repertoire. Current challenges in this field, including the integration of spatial considerations and environmental influences like light and temperature, are also discussed. The review emphasizes the ongoing need for models that balance molecular detail with practical simplicity.

Various condensed phases of water, spanning from the liquid state to multiple ice phases, have been systematically investigated under extreme conditions of pressure and temperature to delineate their stability boundaries. This study focuses on probing the mechanical stability of liquid water through path-integral molecular dynamics simulations, employing the q-TIP4P/F potential to model interatomic interactions in flexible water molecules. Temperature and pressure conditions ranging from 250 to 375 K and -0.3 to 1 GPa, respectively, are considered. This comprehensive approach enables a thorough exploration of nuclear quantum effects on various physical properties of water through direct comparisons with classical molecular dynamics results employing the same potential model. Key properties such as molar volume, intramolecular bond length, H--O--H angle, internal and kinetic energy are analyzed, with a specific focus on the effect of tensile stress. Particular attention is devoted to the liquid-gas spinodal pressure, representing the limit of mechanical stability for the liquid phase, at several temperatures. The quantum simulations reveal a spinodal pressure for water of -286 and -236 MPa at temperatures of 250 and 300 K, respectively. At these temperatures, the discernible shifts induced by nuclear quantum motion are quantified at 15 and 10 MPa, respectively. These findings contribute valuable insights into the interplay of quantum effects on the stability of liquid water under diverse thermodynamic conditions.

Quantum field theory predicts a nonlinear response of the vacuum to strong electromagnetic fields of macroscopic extent. This fundamental tenet has remained experimentally challenging and is yet to be tested in the laboratory. A particularly distinct signature of the resulting optical activity of the quantum vacuum is vacuum birefringence. This offers an excellent opportunity for a precision test of nonlinear quantum electrodynamics in an uncharted parameter regime. Recently, the operation of the high-intensity laser ReLaX provided by the Helmholtz International Beamline for Extreme Fields (HIBEF) has been inaugurated at the High Energy Density (HED) scientific instrument of the European XFEL. We make the case that this worldwide unique combination of an x-ray free-electron laser and an ultra-intense near-infrared laser together with recent advances in high-precision x-ray polarimetry, refinements of prospective discovery scenarios, and progress in their accurate theoretical modelling have set the stage for performing an actual discovery experiment of quantum vacuum nonlinearity.

In order to enhance safety, nuclear reactors in the design phase consider natural circulation as a mean to remove residual power. The simulation of this passive mechanism must be qualified between the validation range and the scope of utilization (reactor case), introducing potential physical and numerical distortion effects. In this study, we simulate the flow of liquid sodium using the TrioCFD code, employing both higher-fidelity (HF) LES and lower-fidelity (LF) URANS models. We tackle respectively numerical uncertainties through the Grid Convergence Index method, and physical modelling uncertainties through the Polynomial Chaos Expansion method available on the URANIE platform. HF simulations are shown to exhibit a strong resilience to physical distortion effects, with numerical uncertainties being intricately correlated. Conversely, the LF approach, the only one applicable at the reactor scale, is likely to present a reduced predictability. If so, the HF approach should be effective in pinpointing the LF weaknesses: the concept of scaling uncertainty is inline introduced as the growth of the LF simulation uncertainty associated with distortion effects. Thus, the paper outlines that a specific methodology within the BEPU framework - leveraging both HF and LF approaches - could pragmatically enable correlating distortion effects with scaling uncertainty, thereby providing a metric principle.

We measure the scalar, vector and tensor components of the differential dynamic polarizability of the strontium intercombination transition at 1064 nm. We compare the experimental values with the theoretical prediction based on the most recently published spectroscopic data, and find a very good agreement. We also identify a close-to-circular `magic' polarization where the differential polarizability strictly vanishes, and precisely determine its ellipticity. Our work opens new perspectives for laser cooling optically trapped strontium atoms, and provides a new benchmark for atomic models in the near infrared spectral range.

The 4H-SiC material exhibits good detection performance, but there are still many problems like signal distortion and poor signal quality. The 4H-SiC low gain avalanche detector (LGAD) has been fabricated for the first time to solve these problems, which named SICAR (SIlicon CARbide). The results of electrical characteristics and charge collection performance of the 4H-SiC LGAD are reported. The influence of different metal thicknesses on the leakage current of the device is studied.~By optimizing the fabrication process, the leakage current of the detector is reduced by four orders of magnitude. The experimental results confirm this 4H-SiC LGAD has an obvious gain structure, the gain factor of the SICAR is reported to be about 2 at 150 V. The charge collection efficiency (CCE) of the device was analyzed using $\alpha$ particle incidence of 5.54 MeV, and the CCE is 90\% @100~V. This study provides a novel 4H-SiC LGAD radiation detector for application in the field of high energy particle physics.

In this contribution, we address the core of any Fourier transform (FT) spectrometer$\unicode{x2013}$the interferometer$\unicode{x2013}$in perspective of the recent emergence of spatially coherent broadband infrared (IR) sources. As a result, we report on the design of a wavefront-division interferometer for spectroscopic applications in the mid-IR and beyond. The theoretical framework of the proposed wavefront division interferometer is discussed, and an analytical solution to determine the far-field interference pattern is derived. The solution is verified by both optical propagation simulations and experimentally. In view of the practical significance, we apply the wavefront division interferometer for FTIR spectroscopy. It features a simple architecture, ultra-broad achromaticity (limited only by the spectral profiles of the mirrors), high optical throughput, variable arms split ratio, and a two-fold increase in scan length and spectral resolution (demonstrated up to 0.2 $cm^{-1}$), respectively. Further, the employed design inherently enables the measurement of the complex refractive index. Experimental verification of the mentioned properties is provided by coupling the spectrometer with a mid-IR supercontinuum source for various applied spectroscopic studies: high-resolution transmission measurements of polymers (polypropylene) and gas (methane), as well as reflectance measurements of dried pharmaceuticals (insulin products on a metal surface).

Free energies play a central role in characterising the behaviour of chemical systems and are among the most important quantities that can be calculated by molecular dynamics simulations. The free energy of hydration in particular is a well-studied physicochemical property of drug-like molecules and is commonly used to assess and optimise the accuracy of nonbonded parameters in empirical forcefields, and as a fast-to-compute surrogate of performance for protein-ligand binding free energy estimation. Machine learned potentials (MLPs) show great promise as more accurate alternatives to empirical forcefields, but are not readily decomposed into physically motivated functional forms, which has thus far rendered them incompatible with standard alchemical free energy methods that manipulate individual pairwise interaction terms. However, since the accuracy of free energy calculations is highly sensitive to the forcefield, this is a key area in which MLPs have the potential to address the shortcomings of empirical forcefields. In this work, we introduce an efficient alchemical free energy method compatible with MLPs, enabling, for the first time, calculations of biomolecular free energy with \textit{ab initio} accuracy. Using a pretrained, transferrable, alchemically equipped MACE model, we demonstrate sub-chemical accuracy for the hydration free energies of organic molecules.

Bulk viscosity of a plasma consisting of strongly coupled diatomic ions is computed using molecular dynamics simulations. The simulations are based on the rigid rotor one-component plasma, which is introduced as a model system that adds two degrees of molecular rotation to the traditional one-component plasma. It is characterized by two parameters: the Coulomb coupling parameter, $\Gamma$, and the bond length parameter, $\Omega$. Results show that the long-range nature of the Coulomb potential can lead to long rotational relaxation times, which in turn yield large values for bulk viscosity. The bulk-to-shear viscosity ratio is found to span from small to large values depending on the values of $\Gamma$ and $\Omega$. Although bulk viscosity is often neglected in plasma modeling, these results motivate that it can be large in molecular plasmas with rotational degrees of freedom.

It has been theoretically predicted that the \ExB drift caused by the spontaneously generated potential in scrape-off-layers (SOLs) and divertors in tokamaks is of a similar size to the poloidal component of the parallel flow and turbulent flow, thereby it significantly impacts on the plasma transport there. Many experiments indeed have implied the role of the electric potential, however, its direct observation through its \ExB flow measurement has never been realized because the drift velocity ($10^2$--$10^3$ m/s) is significantly below the detection limit of existing diagnostics. To realize a cross-field ion flow measurement, variety of systematic uncertainties of the system must be narrowed down. Here, we develop a conceptual design of the Doppler spectrometry that enables to measure the impurity flows with $10^2$-m/s accuracy, based on an in-situ wavelength-calibration techniques developed in astrophysics field, the iodine-cell method. We discuss its properties and applicability. In particular, the scaling relation of the wavelength accuracy and various spectroscopic parameters is newly presented, which suggests the high importance of the wavelength resolution of the system. Based on transport simulations for the JT-60SA divertor, the feasibility of the system is assessed.

The adsorption and desorption of reactants and products from a solid surface is essential for achieving sustained surface chemical reactions. At a liquid-solid interface, these processes can involve the collective reorganization of interfacial solvent molecules in order to accommodate the adsorbing or desorbing species. Identifying the role of solvent in adsorption and desorption is important for advancing our understanding of surface chemical rates and mechanisms and for enabling the rational design and optimization of surface chemical systems. In this manuscript we use all-atom molecular dynamics simulation and transition path sampling to identify water's role in the desorption of CO from a Pt(100) surface in contact with liquid water. We demonstrate that the solvation of CO, as quantified by the water coordination number, is an essential component of the desorption reaction coordinate. We use meta dynamics to compute the desorption free energy surface and conclude based on its features that desorption proceeds via a two-step mechanism whereby the final detachment of CO from the surface is preceded by the formation of a nascent solvation shell.

Vertical equilibrium models have proven to be well suited for simulating fluid flow in subsurface porous media such as saline aquifers with caprocks. However, in most cases the dimensionally reduced model lacks the accuracy to capture the dynamics of a system. While conventional full-dimensional models have the ability to represent dynamics, they come at the cost of high computational effort. We aim to combine the efficiency of the vertical equilibrium model and the accuracy of the full-dimensional model by coupling the two models adaptively in a unified framework and solving the emerging system of equations in a monolithic, fully-implicit approach. The model domains are coupled via mass-conserving fluxes while the model adaptivity is ruled by adaption criteria. Overall, the adaptive model shows an excellent behaviour both in terms of accuracy as well as efficiency, especially for elongated geometries of storage systems with large aspect ratios.

Terahertz (THz) imaging is one of the hotspots in the field of optics, where the depth information retrieval is a key factor to restore the three-dimensional appearance of objects. Impressive results for depth extraction in visible and infrared wave range have been demonstrated through deep learning (DL). Among them, most DL methods are merely data-driven, lacking relevant physical priors, which thus request for a large amount of experimental data to train the DL models.However, large training data acquirement in the THz domain is challenging due to the requirements of environmental and system stability, as well as the time-consuming data acquisition process. To overcome this limitation, this paper incorporates a complete physical model representing the THz image formation process into traditional DL networks to retrieve the depth information of objects. The most significant advantage is the ability to use it without pre-training, thereby eliminating the need for tens of thousands of labeled data. Through experiments validation, we demonstrate that by providing diffraction patterns of planar objects with their upper and lower halves individually masked, the proposed physics-informed neural network (NN) can automatically optimize and, ultimately, reconstruct the depth of the object through interaction between the NN and a physical model. The obtained results represent the initial steps towards achieving fast holographic THz imaging using reference-free beams and low-cost power detection.

Nanomechanical resonators can serve as ultrasensitive, miniaturized force probes. While vertical structures like nanopillars are ideal for this purpose, transducing their motion is challenging. Pillar-based photonic crystals (PhCs) offer a potential solution by integrating optical transduction within the pillars. However, achieving high-quality PhCs is hindered by inefficient vertical light confinement. Here, we present a full-silicon 1D photonic crystal cavity based on nanopillars as a new platform with great potential for applications in force sensing and biosensing areas. Its unit cell consists of a silicon pillar with larger diameter at its top portion than at the bottom, which allows vertical light confinement and an energy bandgap in the near infrared range for transverse-magnetic (TM) polarization. We experimentally demonstrate optical cavities with Q-factors exceeding 1e3 constructed by inserting a defect within a periodic arrangement of this type of pillars. Given the fact that that each nanopillar naturally behaves as a nanomechanical cantilever, the fabricated geometries are excellent optomechanical (OM) photonic crystal cavities in which the mechanical motion of each nanopillar composing the cavity can be optically transduced. These novel geometries display enhanced mechanical properties, cost-effectiveness, integration possibilities, and scalability, and opens and new path in front of the widely used suspended Si beam OM cavities made on silicon-on-insulator.

This research introduces a novel anomaly detection method designed to enhance the operational reliability of particle accelerators - complex machines that accelerate elementary particles to high speeds for various scientific applications. Our approach utilizes a Long Short-Term Memory (LSTM) neural network to predict the temperature of key components within the magnet power supplies (PSs) of these accelerators, such as heatsinks, capacitors, and resistors, based on the electrical current flowing through the PS. Anomalies are declared when there is a significant discrepancy between the LSTM-predicted temperatures and actual observations. Leveraging a custom-built test stand, we conducted comprehensive performance comparisons with a less sophisticated method, while also fine-tuning hyperparameters of both methods. This process not only optimized the LSTM model but also unequivocally demonstrated the superior efficacy of this new proposed method. The dedicated test stand also facilitated exploratory work on more advanced strategies for monitoring interior PS temperatures using infrared cameras. A proof-of-concept example is provided.

E. coli use a regular lattice of receptors and attached kinases to detect and amplify faint chemical signals. Kinase output is characterized by precise adaptation to a wide range of background ligand levels and large gain in response to small relative changes in ligand concentration. These characteristics are well described by models which achieve their gain through equilibrium cooperativity. But these models are challenged by two experimental results. First, neither adaptation nor large gain are present in receptor binding assays. Second, in cells lacking adaptation machinery, fluctuations can sometimes be enormous, with essentially all kinases transitioning together. Here we introduce a far-from equilibrium model in which receptors gate the spread of activity between neighboring kinases. This model achieves large gain through a mechanism we term lattice ultrasensitivity (LU). In our LU model, kinase and receptor states are separate degrees of freedom, and kinase kinetics are dominated by chemical rates far-from-equilibrium rather than by equilibrium allostery. The model recapitulates the successes of past models, but also matches the challenging experimental findings. Importantly, unlike past lattice critical models, our LU model does not require parameters to be fine tuned for function.

The relaxation time of several second generation molecular motors is analysed by calculating the minimum energy path between the metastable and stable states and evaluating the transition rate within harmonic transition state theory based on energetics obtained from density functional theory. Comparison with published experimental data shows remarkably good agreement and demonstrates the predictive capability of the theoretical approach. While previous measurements by Feringa and coworkers [Chem.\,Eur.\,J.\,(2017) 23, 6643] have shown that a replacement of the stereogenic hydrogen by a fluorine atom increases the relaxation time because of destabilization of the transition state for the thermal helix inversion, we find that a replacement of CH$_3$ by a CF$_3$ group at the same site shortens the relaxation time because of elevated energy of the metastable state without a significant shift in the transition state energy. Since these two fluorine substitutions have an opposite effect on the relaxation time, the two combined can provide a way to fine tune the rotational speed of a molecular motor.

Morphogenesis is the process whereby the body of an organism develops its target shape. The morphogen BMP is known to play a conserved role across bilaterian organisms in determining the dorsoventral (DV) axis. Yet, how BMP governs the spatio-temporal dynamics of cytoskeletal proteins driving morphogenetic flow remains an open question. Here, we use machine learning to mine a morphodynamic atlas of Drosophila development, and construct a mathematical model capable of predicting the coupled dynamics of myosin, E-cadherin, and morphogenetic flow. Mutant analysis shows that BMP sets the initial condition of this dynamical system according to the following signaling cascade: BMP establishes DV pair-rule-gene patterns that set-up an E-cadherin gradient which in turn creates a myosin gradient in the opposite direction through mechanochemical feedbacks. Using neural tube organoids, we argue that BMP, and the signaling cascade it triggers, prime the conserved dynamics of neuroectoderm morphogenesis from fly to humans.

In this article, we review the interdisciplinary techniques (borrowed from physics, mathematics, statistics, machine-learning, etc.) and methodological framework that we have used to understand climate systems, which serve as examples of "complex systems". We believe that this would offer valuable insights to comprehend the complexity of climate variability and pave the way for drafting policies for action against climate change, etc. Our basic aim is to analyse time-series data structures across diverse climate parameters, extract Fourier-transformed features to recognize and model the trends/seasonalities in the climate variables using standard methods like detrended residual series analyses, correlation structures among climate parameters, Granger causal models, and other statistical machine-learning techniques. We cite and briefly explain two case studies: (i) the relationship between the Standardised Precipitation Index (SPI) and specific climate variables including Sea Surface Temperature (SST), El Ni\~no Southern Oscillation (ENSO), and Indian Ocean Dipole (IOD), uncovering temporal shifts in correlations between SPI and these variables, and reveal complex patterns that drive drought and wet climate conditions in South-West Australia; (ii) the complex interactions of North Atlantic Oscillation (NAO) index, with SST and sea ice extent (SIE), potentially arising from positive feedback loops.

The Perdew-Zunger (PZ) self-interaction correction (SIC) is an established tool to correct unphysical behavior in density functional approximations. Yet, PZ-SIC is well-known to sometimes break molecular symmetries. An example of this is the benzene molecule, for which PZ-SIC predicts a symmetry-broken electron density and molecular geometry, since the method does not describe the two possible Kekul\'e structures on an even footing, leading to local minima [Lehtola et al, J. Chem. Theory Comput. 2016, 12, 3195]. PZ-SIC is often implemented with Fermi-L\"owdin orbitals (FLOs), yielding the FLO-SIC method, which likewise has issues with symmetry breaking and local minima [Trepte et al, J. Chem. Phys. 2021, 155, 224109]. In this work, we propose a generalization of PZ-SIC - the ensemble PZ-SIC (E-PZ-SIC) method - which shares the asymptotic computational scaling of PZ-SIC (albeit with an additional prefactor). E-PZ-SIC is straightforwardly applicable to various molecules, merely requiring one to average the self-interaction correction over all possible Kekul\'e structures, in line with chemical intuition. We showcase the implementation of E-PZ-SIC with FLOs, as the resulting E-FLO-SIC method is easy to realize on top of an existing implementation of FLO-SIC. We show that E-FLO-SIC indeed eliminates symmetry breaking, reproducing a symmetric electron density and molecular geometry for benzene. The ensemble approach suggested herein could also be employed within locally scaled variants of PZ-SIC and their FLO-SIC versions.

The Scintillating Bubble Chamber (SBC) collaboration purchased 32 Hamamatsu VUV4 silicon photomultipliers (SiPMs) for use in SBC-LAr10, a bubble chamber containing 10~kg of liquid argon. The VUV4 SiPMs, or Quads, underwent a characterization at two temperatures which measured the breakdown voltage ($V_{\text{BD}}$), the SiPM gain ($g_{\text{SiPM}}$), the rate of change of $g_{\text{SiPM}}$ with respect to voltage ($m$), the dark count rate (DCR), and the probability of a correlated avalanche (P$_{\text{CA}}$) as well as the temperature coefficients of these parameters. A Peltier-based chilled vacuum chamber was developed at Queen's University to cool down the Quads to $233.15\pm0.2$~K and $255.15\pm0.2$~K with average stability of $\pm20$~mK. A mostly assumption-free analysis was derived to estimate $V_{\text{BD}}$ to tens of mV precision and DCR close to Poissonian error. The temperature dependence of $V_{\text{BD}}$ was found to be $56\pm2$~mV~K$^{-1}$, and $m$ on average across all Quads was found to be $(459\pm3(\rm{stat.})\pm23(\rm{sys.}))\times 10^{3}~e^-$~PE$^{-1}$~V$^{-1}$. The average DCR temperature coefficient was estimated to be $0.099\pm0.008$~K$^{-1}$ corresponding to a reduction factor of 7 for every 20~K drop in temperature. The average temperature dependence of P$_{\text{CA}}$ was estimated to be $4000\pm1000$~ppm~K$^{-1}$. P$_{\text{CA}}$ estimated from the average across all SiPMs is a better estimator than the P$_{\text{CA}}$ calculated from individual SiPMs, whereas all of the other parameters, the opposite is true. All the estimated parameters were measured to the precision required for SBC-LAr10, and the Quads will be used in conditions to optimize the signal-to-noise ratio.

We introduce a machine learning (ML) supervised model function that is inspired by the variational principle of physics. This ML hypothesis evolutionary method, termed ML-Omega, allows us to go from data to differential equation(s) underlying the physical (chemical, engineering, etc.) phenomena the data are derived from. The fundamental equations of physics can be derived from this ML-Omega evolutionary method when provided the proper training data. By training the ML-Omega model function with only three hydrogen-like atom energies, the method can find Schr\"odinger's exact functional and, from it, Schr\"odinger's fundamental equation. Then, in the field of density functional theory (DFT), when the model function is trained with the energies from the known Thomas-Fermi (TF) formula E = -0.7687Z^7/3, it correctly finds the exact TF functional. Finally, the method is applied to find a local orbital-free (OF) functional expression of the independent electron kinetic energy functional Ts based on the gamma-TF-lambda-vW model. By considering the theoretical energies of only 5 atoms (He, Be, Ne, Mg, Ar) as the training set, the evolutionary ML-Omega method finds an ML-Omega-OF-DFT local Ts functional (gamma-TF-lambda-vW (0.964, 1/4)) that outperforms all the OF- DFT functionals of a representative group. Moreover, our ML-Omega-OF functional overcomes the LDA's and some local GGA-DFT's functionals' difficulty to describe the stretched bond region at the correct spin configuration of diatomic molecules. Although our evolutionary ML-Omega model function can work without an explicit prior-form functional, by using the techniques of symbolic regression, in this work we exploit prior-form functional expressions to make the training process faster in the example problems presented here.

This paper introduces WeatherFormer, a transformer encoder-based model designed to learn robust weather features from minimal observations. It addresses the challenge of modeling complex weather dynamics from small datasets, a bottleneck for many prediction tasks in agriculture, epidemiology, and climate science. WeatherFormer was pretrained on a large pretraining dataset comprised of 39 years of satellite measurements across the Americas. With a novel pretraining task and fine-tuning, WeatherFormer achieves state-of-the-art performance in county-level soybean yield prediction and influenza forecasting. Technical innovations include a unique spatiotemporal encoding that captures geographical, annual, and seasonal variations, adapting the transformer architecture to continuous weather data, and a pretraining strategy to learn representations that are robust to missing weather features. This paper for the first time demonstrates the effectiveness of pretraining large transformer encoder models for weather-dependent applications across multiple domains.

We report the existence of deterministic patterns in plots showing the relationship between the mean and the Fano factor (ratio of variance and mean) of stochastic count data. These patterns are found in a wide variety of datasets, including those from genomics, paper citations, commerce, ecology, disease outbreaks, and employment statistics. We develop a theory showing that the patterns naturally emerge when data sampled from discrete probability distributions is organised in matrix form. The theory precisely predicts the patterns and shows that they are a function of only one variable - the sample size.

The magnetic fields that emerge from beneath the solar surface and permeate the solar atmosphere are the key drivers of space weather and, thus, understanding them is important to human society. Direct observations, used to measure magnetic fields, can only probe the magnetic fields in the photosphere and above, far from the regions the magnetic fields are being enhanced by the solar dynamo. Solar gamma rays produced by cosmic rays interacting with the solar atmosphere have been detected from GeV to TeV energy range, and revealed that they are significantly affected by solar magnetic fields. However, much of the observations are yet to be explained by a physical model. Using a semi-analytic model, we show that magnetic fields at and below the photosphere with a large horizontal component could explain the $\sim$1 TeV solar gamma rays observed by HAWC. This could allow high-energy solar gamma rays to be a novel probe for magnetic fields below the photosphere.

Different models of dark matter can alter the distribution of mass in galaxy clusters in a variety of ways. However, so can uncertain astrophysical feedback mechanisms. Here we present a Machine Learning method that ''learns'' how the impact of dark matter self-interactions differs from that of astrophysical feedback in order to break this degeneracy and make inferences on dark matter. We train a Convolutional Neural Network on images of galaxy clusters from hydro-dynamic simulations. In the idealised case our algorithm is 80% accurate at identifying if a galaxy cluster harbours collisionless dark matter, dark matter with ${\sigma}_{\rm DM}/m = 0.1$cm$^2/$g or with ${\sigma}_{DM}/m = 1$cm$^2$/g. Whilst we find adding X-ray emissivity maps does not improve the performance in differentiating collisional dark matter, it does improve the ability to disentangle different models of astrophysical feedback. We include noise to resemble data expected from Euclid and Chandra and find our model has a statistical error of < 0.01cm$^2$/g and that our algorithm is insensitive to shape measurement bias and photometric redshift errors. This method represents a new way to analyse data from upcoming telescopes that is an order of magnitude more precise and many orders faster, enabling us to explore the dark matter parameter space like never before.

This paper presents the design and characterization of a rectangular microstrip patch antenna array optimized for operation within the Ku-band frequency range. The antenna array is impedance-matched to 50 Ohms and utilizes a microstrip line feeding mechanism for excitation. The design maintains compact dimensions, with the overall antenna occupying an area of 29.5x7 mm. The antenna structure is modelled on an R03003 substrate material, featuring a dielectric constant of 3, a low-loss tangent of 0.0009, and a thickness of 1.574 mm. The substrate is backed by a conducting ground plane, and the array consists of six radiating patch elements positioned on top. Evaluation of the designed antenna array reveals a resonant frequency of 18GHz, with a -10 dB impedance bandwidth extending over 700MHz. The antenna demonstrates a high gain of 7.51dBi, making it well-suited for applications in 5G and future communication systems. Its compact form factor, cost-effectiveness, and broad impedance and radiation coverage further underscore its potential in these domains.

An electron-multiplying charge-coupled device (EMCCD) is often used for taking images with space telescopes and other devices. Photons hit the pixels and photo-electrons are created, and these are multiplied via impact ionization as they travel through the gain register from one gain stage to the next. A high gain means a high multiplication factor, and this is achieved through a high voltage difference across a gain stage. If the gain is high enough, the chance of clock-induced charge production in the gain register increases. The probability distribution function governing the gain process in the literature only accounts for charge multiplication if one or more electrons enters the gain register. I derive from first principles the modified probability distribution that accounts for clock-induced charge production in the gain register. I also examine some EMCCD data and show through maximum likelihood estimation that the data conform better to the modified distribution versus the usual one in the literature. The use of the modified distribution would in principle improve the accuracy of signal extraction from a frame.

Full Waveform Inversion (FWI) is an inverse problem for estimating the wave velocity distribution in a given domain, based on observed data on the boundaries. The inversion is computationally demanding because we are required to solve multiple forward problems, either in time or frequency domains, to simulate data that are then iteratively fitted to the observed data. We consider FWI in the frequency domain, where the Helmholtz equation is used as a forward model, and its repeated solution is the main computational bottleneck of the inversion process. To ease this cost, we integrate a learning process of an encoder-solver preconditioner that is based on convolutional neural networks (CNNs). The encoder-solver is trained to effectively precondition the discretized Helmholtz operator given velocity medium parameters. Then, by re-training the CNN between the iterations of the optimization process, the encoder-solver is adapted to the iteratively evolving velocity medium as part of the inversion. Without retraining, the performance of the solver deteriorates as the medium changes. Using our light retraining procedures, we obtain the forward simulations effectively throughout the process. We demonstrate our approach to solving FWI problems using 2D geophysical models with high-frequency data.

This paper presents a study on a compartmental epidemic model for COVID-19, examining the stability of its equilibrium points upon the introduction of vaccination as a strategy to mitigate the spread of the disease. Initially, the SIQR (Susceptible-Infectious-Quarantine-Recovered) mathematical model and its technical aspects are introduced. Subsequently, vaccination is incorporated as a control measure within the model scope. Equilibrium points and the basic reproductive number are determined, followed by an analysis of their stability. Furthermore, controllability characteristics and Optimal Control strategies for the system are investigated, supplemented by numerical simulations.

The redshifted 21 cm line signal is a powerful probe of the cosmic dawn and the epoch of reionization. The global spectrum can potentially be detected with a single antenna and spectrometer. However, this measurement requires an extremely accurate calibration of the instrument to facilitate the separation of the 21 cm signal from the much brighter foregrounds and possible variations in the instrument response. Understanding how the measurement errors propagate in a realistic instrument system and affect system calibration is the focus of this work. We simulate a 21 cm global spectrum observation based on the noise wave calibration scheme. We focus on how measurement errors in reflection coefficients affect the noise temperature and how typical errors impact the recovery of the 21 cm signal, especially in the frequency domain. Results show that for our example set up, a typical vector network analyzer (VNA) measurement error in the magnitude of the reflection coefficients of the antenna, receiver, and open cable, which are 0.001, 0.001, and 0.002 (linear), respectively, would result in a 200 mK deviation on the detected signal, and a typical measurement error of 0.48 degree, 0.78 degree, or 0.15 degree in the respective phases would cause a 40 mK deviation. The VNA measurement error can greatly affect the result of a 21 cm global spectrum experiment using this calibration technique, and such a feature could be mistaken for or be combined with the 21 cm signal

Motion artifacts in Magnetic Resonance Imaging (MRI) are one of the frequently occurring artifacts due to patient movements during scanning. Motion is estimated to be present in approximately 30% of clinical MRI scans; however, motion has not been explicitly modeled within deep learning image reconstruction models. Deep learning (DL) algorithms have been demonstrated to be effective for both the image reconstruction task and the motion correction task, but the two tasks are considered separately. The image reconstruction task involves removing undersampling artifacts such as noise and aliasing artifacts, whereas motion correction involves removing artifacts including blurring, ghosting, and ringing. In this work, we propose a novel method to simultaneously accelerate imaging and correct motion. This is achieved by integrating a motion module into the deep learning-based MRI reconstruction process, enabling real-time detection and correction of motion. We model motion as a tightly integrated auxiliary layer in the deep learning model during training, making the deep learning model 'motion-informed'. During inference, image reconstruction is performed from undersampled raw k-space data using a trained motion-informed DL model. Experimental results demonstrate that the proposed motion-informed deep learning image reconstruction network outperformed the conventional image reconstruction network for motion-degraded MRI datasets.

This research employs the Kraus representation and Sz.-Nagy dilation theorem to model a three-level quantum heat on quantum circuits, investigating its dynamic evolution and thermodynamic performance. The feasibility of the dynamic model is validated by tracking the changes of population. On the basis of reinforcement learning algorithm, the optimal cycle of the quantum heat engine for maximal average power is proposed and verified by the thermodynamic model. The stability of quantum circuit simulations is scrutinized through a comparative analysis of theoretical and simulated results, predicated on an orthogonal test. These results affirm the practicality of simulating quantum heat engines on quantum circuits, offering potential for substantially curtailing the experimental expenses associated with the construction of such engines.

We develop a thermodynamic theory in the non-equilibrium regime by extending a theory based on the dimensionless (DL) minimum work principle previously developed for a thermodynamic system-bath model [S.Koyanagi and Y.Tanimura,J.Chem.Phys.160,(2024)]. Our results are described by non-equilibrium thermodynamic potentials expressed in time-derivative form in terms of extensive and intensive variables. This is made possible through the incorporation of waste heat, which is equivalent to the loss work consumed by the bath, into the definitions of thermodynamic potentials in a non-equilibrium regime. These potentials can be evaluated from the DL non-equilibrium-to-equilibrium minimum work principle, which is derived from the principle of DL minimum work and is equivalent to the second law of thermodynamics. We thus obtain the non-equilibrium Massieu-Planck potentials as entropic potentials and the non-equilibrium Helmholtz-Gibbs potentials as free energies. Our results are verified numerically by simulating a Stirling engine consisting of two isothermal and two thermostatic processes using the quantum Fokker-Planck equations and the classical Kramers equation derived from the thermodynamic system-bath model. We then show that any thermodynamic process can be analyzed using a non-equilibrium work diagram analogous to the equilibrium one for given time-dependent intensive variables. The results can be used to develop efficient heat machines in non-equilibrium regimes.

Magic angle twisted bilayer graphene (MATBG) presents a fascinating platform for investigating the effects of electron interactions in topological flat bands. The Bistritzer-MacDonald (BM) model provides a simplified quantitative description of the flat bands. Introducing long-range Coulomb interactions leads to an interacting BM (IBM) Hamiltonian, a momentum-space continuum description which offers a very natural starting point for many-body studies of MATBG. Accurate and reliable many-body computations in the IBM model are challenging, however, and have been limited mostly to special fillings, or smaller lattice sizes. We employ state-of-the-art auxiliary-field quantum Monte Carlo (AFQMC) method to study the IBM model, which constrains the sign problem to enable accurate treatment of large system sizes. We determine ground-state properties and quantify errors compared to mean-field theory calculations. Our calculations identify correlated metal states and their competition with the insulating Kramers inter-valley coherent state at both half-filling and charge neutrality. Additionally, we investigate one- and three-quarter fillings, and examine the effect of many-body corrections beyond single Slater determinant solutions. We discuss the effect that details of the IBM Hamiltonian have on the results, including different forms of double-counting corrections, and the need to establish and precisely specify many-body Hamiltonians to allow more direct and quantitative comparisons with experiments in MATBG.

Our analysis suggests strongly that stationary pulses exist in nonlinear media with second-, third-, and fourth-order dispersion. A theory, based on the variational approach, is developed for finding approximate parameters of such solitons. It is obtained that the soliton velocity in the retarded reference frame can be different from the inverse of the group velocity of linear waves. It is shown that the interaction of the pulse spectrum with that of linear waves can affect the existence of stationary solitons. These theoretical results are supported by numerical simulations. Transformations between solitons of different systems are derived. A generalization for solitons in media with the highest even-order dispersion is suggested.

This paper presents the energy resolution study in the JUNO experiment, incorporating the latest knowledge acquired during the detector construction phase. The determination of neutrino mass ordering in JUNO requires an exceptional energy resolution better than 3\% at 1 MeV. To achieve this ambitious goal, significant efforts have been undertaken in the design and production of the key components of the JUNO detector. Various factors affecting the detection of inverse beta decay signals have an impact on the energy resolution, extending beyond the statistical fluctuations of the detected number of photons, such as the properties of liquid scintillator, performance of photomultiplier tubes, and the energy reconstruction algorithm. To account for these effects, a full JUNO simulation and reconstruction approach is employed. This enables the modeling of all relevant effects and the evaluation of associated inputs to accurately estimate the energy resolution. The study reveals an energy resolution of 2.95\% at 1 MeV. Furthermore, the study assesses the contribution of major effects to the overall energy resolution budget. This analysis serves as a reference for interpreting future measurements of energy resolution during JUNO data taking. Moreover, it provides a guideline in comprehending the energy resolution characteristics of liquid scintillator-based detectors.

We present the model we developed to reconstruct the CUORE radioactive background based on the analysis of an experimental exposure of 1038.4 kg yr. The data reconstruction relies on a simultaneous Bayesian fit applied to energy spectra over a broad energy range. The high granularity of the CUORE detector, together with the large exposure and extended stable operations, allow for an in-depth exploration of both spatial and time dependence of backgrounds. We achieve high sensitivity to both bulk and surface activities of the materials of the setup, detecting levels as low as 10 nBq kg$^{-1}$ and 0.1 nBq cm$^{-2}$, respectively. We compare the contamination levels we extract from the background model with prior radio-assay data, which informs future background risk mitigation strategies. The results of this background model play a crucial role in constructing the background budget for the CUPID experiment as it will exploit the same CUORE infrastructure.

Electric field-induced modulation of the optical properties is crucial for amplitude and phase modulators used in photonic devices. Here, we present a comprehensive study of the band geometry-induced electro-optic effect, specifically focusing on the Fermi surface and disorder-induced contributions. These contributions are crucial for metallic and semimetallic systems and for optical frequencies comparable to or smaller than the scattering rates. We highlight the importance of the quantum metric and metric connection in generating the electro-optic effect in parity-time reversal ($\mathcal{PT}$) symmetric systems such as CuMnAs. Our findings establish the electro-optic effect as a novel tool to probe band geometric effects and open new avenues to design electrically controlled efficient amplitude and phase modulators for photonic applications.

Coronal Mass Ejections (CMEs) are the main drivers of the disturbances in interplanetary space. Understanding the CME interior magnetic structure is crucial for advancing space weather studies. Assessing the capabilities of a numerical heliospheric model is crucial, as understanding the nature and extent of its limitations can be used for improving the model and the space weather predictions based on it. The present paper aims to test the capabilities of the recently developed heliospheric model Icarus and the linear force-free spheromak model that has been implemented in it. To validate the Icarus space weather modeling tool, two CME events were selected that were observed by two spacecraft located near Mercury and Earth, respectively. This enables testing the heliospheric model computed with Icarus at two distant locations. The source regions for the CMEs were identified, and the CME parameters were determined and later optimized. Different adaptive mesh refinement levels were applied in the simulations to assess its performance by comparing the simulation results to in-situ measurements. The first CME event erupted on SOL2013-07-09T15:24. The modeled time series were in good agreement with the observations both at MESSENGER and ACE. The second CME event started on SOL2014-02-16T10:24 and was more complicated, as three CME interactions occurred in this event. It was impossible to recover the observed profiles without modeling the other two CMEs that were observed, one before the main CME and one afterward. For both CME studies, AMR level 3 was sufficient to reconstruct small-scale features near Mercury, while at Earth, AMR level 4 was necessary due to the radially stretched grid that was used.

Systematic studies of the gradual fabrication by means of carbon ion-implantation of high-quality 6H-SiC layers on silicon surfaces have been carried out. The fluence of carbon ions varied from 5*10^15 cm-2 to 10^17 cm-2. Results of first-principle calculations, X-ray diffraction (XRD), and Raman spectroscopy demonstrate the amorphization of silicon substrate without any tendency to the segregation of carbon in the samples synthesized at low fluencies. The formation of a SiO2-like structure at this stage was also detected. X-ray photoelectron spectroscopy (XPS), XRD, and Raman spectroscopy demonstrate that an increase in carbon content at 10^17 cm-2 fluence leads to the growth of 6H-SiC films on the surface of the amorphous silicon substrate. Atomic force microscopy (AFM) data obtained also demonstrates the decreasing of surface roughens after the formation of SiC film. XPS and Raman spectra suggest that excessive carbon content leaves the SiC matrix via the formation of an insignificant amount of partially oxidized carbon nanostructures. Optical measurements also support the claim of high-quality 6H-SiC film formation in the samples synthesized at 10^17 cm-2 fluence and demonstrate the absence of any detectable contribution of nanostructures formed from excessive carbon on the optical properties of the material under study.

Machine learning (ML) methods are having a huge impact across all of the sciences. However, ML has a strong ontology - in which only the data exist - and a strong epistemology - in which a model is considered good if it performs well on held-out training data. These philosophies are in strong conflict with both standard practices and key philosophies in the natural sciences. Here, we identify some locations for ML in the natural sciences at which the ontology and epistemology are valuable. For example, when an expressive machine learning model is used in a causal inference to represent the effects of confounders, such as foregrounds, backgrounds, or instrument calibration parameters, the model capacity and loose philosophy of ML can make the results more trustworthy. We also show that there are contexts in which the introduction of ML introduces strong, unwanted statistical biases. For one, when ML models are used to emulate physical (or first-principles) simulations, they introduce strong confirmation biases. For another, when expressive regressions are used to label datasets, those labels cannot be used in downstream joint or ensemble analyses without taking on uncontrolled biases. The question in the title is being asked of all of the natural sciences; that is, we are calling on the scientific communities to take a step back and consider the role and value of ML in their fields; the (partial) answers we give here come from the particular perspective of physics.

Brillouin-based optomechanical systems with high-frequency acoustic modes provide a promising platform for implementing quantum-information processing and wavelength conversion applications, and for probing macroscopic quantum effects. Achieving strong coupling through electrostrictive Brillouin interaction is essential for coupling the massive mechanical mode to an optical field, thereby controlling and characterizing the mechanical state. However, achieving strong coupling at room temperature has proven challenging due to fast mechanical decay rates, which increase the pumping power required to surpass the coupling threshold. Here, we report an optomechanical system with independent control over pumping power and frequency detuning to achieve and characterize the strong-coupling regime of a bulk acoustic-wave resonator. Through spectral analysis of the cavity reflectivity, we identify clear signatures of strong coupling, i.e., normal-mode splitting and an avoided crossing in the detuned spectra, while estimating the mechanical linewidth $\Gamma_m/2\pi~=~7.13MHz$ and the single-photon coupling rate $g_0/2\pi~=~7.76Hz$ of our system. Our results provide valuable insights into the performances of room-temperature macroscopic mechanical systems and their applications in hybrid quantum devices.

Rising inequalities around the globe bring into question our economic systems and the origin of such inequalities. Here we propose a toy agent-based model where each entity is simultaneously producing and consuming indivisible goods. We find that the system exhibits a non-trivial phase transition beyond which a market clearing equilibrium exists but becomes dynamically unreachable. When production capacity exceeds a threshold and adapts too slowly, some agents cannot sell all their goods. This leads to global price deflation and induces strong wealth inequalities, with the spontaneous separation of the population into a rich class and a poor class. We explore ways to alleviate poverty in this model and whether they have real life significance.

The long-term evolution of astrophysical systems is driven by a Hamiltonian that is independent of the fast angle. As this Hamiltonian may contain explicitly time-dependent parameters, the conservation of mechanical energy is not guaranteed in such systems. We derive how the semi-major axis evolves in these cases. We analyze two astrophysically interesting examples, those of the harmonic and quadrupole perturbations.

Usually, the strain-induced softening behaviour observed in the differential modulus $K(T,\gamma)$ of hydrogels has been attributed to the breakage of internal structures of the network, such as the cross-links that bind together the polymer chains. Here we consider a stress-strain relationship that we have recently derived from a coarse-grained model to demonstrate that no rupture of the network is needed for rubber-like gels to present such behaviour. In particular, we show that, in some cases, the decreasing of $K(T,\gamma)$ as a function of the strain $\gamma$ is closely related to the energy-related contribution to the elastic modulus that has been experimentally observed, e.g., for tetra-PEG hydrogels. Thus, our results suggest that, instead of the breakage of structures, the softening behaviour can be also related to the effective interaction between the chains in the network and their neighbouring solvent molecules. Comparison to experimental data determined for several hydrogels is included to illustrate that behaviour and to validate our approach.

A fundamental problem of out-of-equilibrium physics is the speed at which the order parameter grows upon crossing a phase transition. Here, we investigate the dynamics of ordering in a Fermi gas undergoing a density-wave phase transition induced by quenching of long-range, cavity-mediated interactions. We observe in real-time the exponential rise of the order parameter and track its growth over several orders of magnitude. Remarkably, the growth rate is insensitive to the contact interaction strength from the ideal gas up to the unitary limit and can exceed the Fermi energy by an order of magnitude, in quantitative agreement with a linearized instability analysis. We then generalize our results to linear interaction ramps, where deviations from the adiabatic behaviour are captured by a simple dynamical ansatz. Our study offers a paradigmatic example of the interplay between non-locality and non-equilibrium dynamics, where universal scaling behaviour emerges despite strong interactions at the microscopic level.

Using low-energy muons, we map the charge carrier concentration as a function of depth and electric field across the \SiOSi interface up to a depth of \SI{100}{\nano\meter} in Si-based MOS capacitors. The results show that the formation of the anisotropic bond-centered muonium \MuBCz state in Si serves as a direct measure of the local changes in electronic structures. Different band-bending conditions could be distinguished, and the extension of the depletion width was directly extracted using the localized stopping and probing depth of the muons. Furthermore, electron build-up on the Si side of the \SiOO/Si interface, caused by the mirror charge induced by the fixed positive charge in the oxide and the image force effect, was observed. Our work represents a significant extension of the application of the muon spin rotation technique ($\mu$SR) and lays the foundation for further research on direct observation of charge carrier density manipulation at technologically important semiconductor device interfaces.

We construct a hydrodynamic theory of active smectics A in two-dimensional space, including the creation/annihilation and motility of dislocations with Burgers' number $\pm1$. We derive analytical criteria on the set of parameters that lead to flows. We show that the motility of dislocations can lead to flow transitions with distinct features from the previously reported active Helfrich--Hurault shear instability with, notably, a first-order transition in the velocity from quiescence to turbulence.

We propose a new type of multi-bit and energy-efficient magnetic memory based on current-driven, field-free, and highly controlled domain wall motion. A meandering domain wall channel with precisely interspersed pinning regions provides the multi-bit capability of a magnetic tunnel junction. The magnetic free layer of the memory device has perpendicular magnetic anisotropy and interfacial Dzyaloshinskii-Moriya interaction, so that spin-orbit torques induce efficient domain wall motion. Using micromagnetic simulations, we find two pinning mechanisms that lead to different cell designs: two-way switching and four-way switching. The memory cell design choices and the physics behind these pinning mechanisms are discussed in detail. Furthermore, we show that switching reliability and speed may be significantly improved by replacing the ferromagnetic free layer with a synthetic antiferromagnetic layer. Switching behavior and material choices will be discussed for the two implementations.

The laws of thermodynamics apply to biophysical systems on the nanoscale as described by the framework of stochastic thermodynamics. This theory provides universal, exact relations for quantities like work, which have been verified in experiments where a fully resolved description allows direct access to such quantities. Complementary studies consider partially hidden, coarse-grained descriptions, in which the mean entropy production typically is not directly accessible but can be bounded in terms of observable quantities. Going beyond the mean, we introduce a fluctuating entropy production that applies to individual trajectories in a coarse-grained description under time-dependent driving. Thus, this concept is applicable to the broad and experimentally significant class of driven systems in which not all relevant states can be resolved. We provide a paradigmatic example by studying an experimentally verified protein unfolding process. As a consequence, the entire distribution of the coarse-grained entropy production rather than merely its mean retains spatial and temporal information about the microscopic process. In particular, we obtain a bound on the distribution of the physical entropy production of individual unfolding events.

Interfacial engineering has fueled recent development of p-i-n perovskite solar cells (PSCs), with self-assembled monolayer-based hole-transport layers (SAM-HTLs) enabling almost lossless contacts for solution-processed PSCs, resulting in the highest achieved power conversion efficiency (PCE) to date. Substrate interfaces are particularly crucial for the growth and quality of co-evaporated PSCs. However, adoption of SAM-HTLs for co-evaporated perovskite absorbers is complicated by the underexplored interaction of such perovskites with phosphonic acid functional groups. In this work, we highlight how exposed phosphonic acid functional groups impact the initial phase and final bulk crystal structures of co-evaporated perovskites and their resultant PCE. The explored surface interaction is mediated by hydrogen bonding with interfacial iodine, leading to increased formamidinium iodide adsorption, persistent changes in perovskite structure, and stabilization of bulk {\alpha}-FAPbI3, hypothesized as being due to kinetic trapping. Our results highlight the potential of exploiting substrates to increase control of co-evaporated perovskite growth.

We present a perturbation approach to calculate the short-time propagator, or transition density, of the one-dimensional Fokker-Planck equation, to in principle arbitrary order in the time increment. Our approach preserves probability exactly and allows us to evaluate expectation values of analytical observables to in principle arbitrary accuracy; to showcase this, we derive perturbation expansions for the moments of the spatial increment, the finite-time Kramers-Moyal coefficients, and the mean medium entropy production rate. For an explicit multiplicative-noise system with available analytical solution, we validate all our perturbative results. Throughout, we compare our perturbative results to those obtained from the widely used Gaussian approximation of the short-time propagator; we demonstrate that this Gaussian propagator leads to errors that can be many orders of magnitude larger than those resulting from our perturbation approach. Potential applications of our results include parametrizing diffusive stochastic dynamics from observed time series, and sampling path spaces of stochastic trajectories numerically.

Machine learning techniques have been widely employed as effective tools in addressing various engineering challenges in recent years, particularly for the challenging task of microstructure-informed materials modeling. This work provides a comprehensive review of the current machine learning-assisted and data-driven advancements in this field, including microstructure characterization and reconstruction, multiscale simulation, correlations among process, microstructure, and properties, as well as microstructure optimization and inverse design. It outlines the achievements of existing research through best practices and suggests potential avenues for future investigations. Moreover, it prepares the readers with educative instructions of basic knowledge and an overview on machine learning, microstructure descriptors and machine learning-assisted material modeling, lowering the interdisciplinary hurdles. It should help to stimulate and attract more research attention to the rapidly growing field of machine learning-based modeling and design of microstructured materials.

Two mechanisms that have been used to study the evolution of cooperative behavior are altruistic punishment, in which cooperative individuals pay additional costs to punish defection, and multilevel selection, in which competition between groups can help to counteract individual-level incentives to cheat. Boyd, Gintis, Bowles, and Richerson have used simulation models of cultural evolution to suggest that altruistic punishment and pairwise group-level competition can work in concert to promote cooperation, even when neither mechanism can do so on its own. In this paper, we formulate a PDE model for multilevel selection motivated by the approach of Boyd and coauthors, modeling individual-level birth-death competition with a replicator equation based on individual payoffs and describing group-level competition with pairwise conflicts based on differences in the average payoffs of the competing groups. Building off of existing PDE models for multilevel selection with frequency-independent group-level competition, we use analytical and numerical techniques to understand how the forms of individual and average payoffs can impact the long-time ability to sustain altruistic punishment in group-structured populations. We find several interesting differences between the behavior of our new PDE model with pairwise group-level competition and existing multilevel PDE models, including the observation that our new model can feature a non-monotonic dependence of the long-time collective payoff on the strength of altruistic punishment. Going forward, our PDE framework can serve as a way to connect and compare disparate approaches for understanding multilevel selection across the literature in evolutionary biology and anthropology.