Papers

Recent advances in metamaterials and fabrication techniques have revived interest in mechanical computing. Contrary to techniques relying on static deformations of buckling beams or origami-based lattices, the integration of wave scattering and mechanical memory presents a promising path toward efficient, low-latency elastoacoustic computing. This work introduces a novel class of multifunctional mechanical computing circuits that leverage the rich dynamics of phononic and locally resonant materials. These circuits incorporate memory-integrated components, realized here via metamaterial cells infused with shape memory alloys which recall stored elastic profiles and trigger specific actions upon thermal activation. A critical advantage of this realization is its synergistic interaction with incident vibroacoustic loads and the inherited high speed of waves, as opposed to recent adaptations of mechanically intelligent systems that employ innately slower mechanisms such as elastomeric shape changes and snap-through bistabilities. Through a proof-of-concept physical implementation, the efficacy and reconfigurability of the wave-based gates are demonstrated via output probes and measured wavefields. Furthermore, the modular design of the fundamental gates can be used as building blocks to construct complex combinational logic circuits, paving the way for sequential logic in wave-based analog computing systems.

Monolithic 3D stackable 1T1C DRAM technology is on the rise, with initial prototypes reported by the industry. This work presents a comprehensive reliability study focusing on the intricate interplay between the row hammer effect and the floating body effect. First, using a TCAD model of a 3D DRAM mini-array, we categorize different cases of adjacent cells and show that the notorious row hammer effect induced by charge migration is significantly mitigated compared to 2D DRAM. However, we found that when incorporating an impact ionization model to account for the floating body characteristics of the silicon access transistor, the capacitive coupling between vertically stacked cells is severely exacerbated. Second, we conduct an in-depth investigation into the floating body effect itself. We systematically examine the dependence of this effect on key device parameters, including body thickness, doping concentration, and gate work function.

Dust dynamics plays a critical role in astrophysical processes and has been modeled in hydrodynamical simulations using various approaches. Among particle-based methods like Smoothed Particle Hydrodynamics (SPH), the One-Fluid model has proven to be highly effective for simulating gas-dust mixtures. This study presents the implementation of the One-Fluid model in OpenGadget3, introducing improvements to the original formulation. These enhancements include time-dependent artificial viscosity and conductivity, as well as a novel treatment of dust diffusion using a pressure-like term. The improved model is tested using a suite of dust dynamics benchmark problems: DUSTYBOX, DUSTYWAVE, and DUSTYSHOCK, with the latter extended to multidimensional scenarios, as well as a dusty Sedov-Taylor blast wave. Additional tests include simulations of Cold Keplerian Disks, dusty protoplanetary disks, and Kelvin-Helmholtz instabilities to evaluate the model's robustness in more complex flows. The implementation successfully passes all standard benchmark tests. It demonstrates stability and accuracy in both simple and complex simulations. The new diffusion term improves the handling of flows with large dust-to-gas ratios and low drag coefficients, although limitations of the One-Fluid model in these regimes remain. The enhanced One-Fluid model is a reliable and robust tool for simulating dust dynamics in OpenGadget3. While it retains some limitations inherent to the original formulation, the introduced improvements expand its applicability and address some challenges in gas-dust dynamics.

The initial value problem is solved for the excitation of long surface gravity waves in a continuously sheared flow. This reveals the presence of a continuous spectrum along side the standard normal modes of gravity wave propagation. An analytical similarity solution for the evolution of the free surface displacement from the continuous spectrum is found for the impulse response to surface excitation. It is demonstrated that the continuous spectrum contribution can be a significant fraction of the surface response, with the amplitude of the continuous spectrum exceeding that of the upstream gravity wave mode for Froude numbers of order unity. The Landau damped mode description of the continuous spectrum is found to provide a link between methods using dispersion relations for phase speeds within the range of the velocity profile, and the variable-shear profiles that do not admit normal modes in this range.

The ALICE Inner Tracking System upgrade (ITS3) will employ stitched, wafer-scale Monolithic Active Pixel Sensors (MAPS) for the first time in high-energy physics. MOSAIX, a fully functional prototype and the final development step before production, measures 266 mm by 19 mm. The chip integrates 144 independently powered pixel matrices, eight 10 Gb/s transmitters, and on-chip power and data distribution. This contribution presents the development of the MOSAIX test system and verification strategy, with emphasis on validating the testing infrastructure before chip availability. The system includes an FPGA that controls MOSAIX and parses its output, and a second MOSAIX emulator FPGA.

We propose, for the first time, a two-dimensional model for the nonlinear coupling of internal gravity and thermal waves in the presence of temperature-dependent density inhomogeneity due to thermal expansion and thermal feedback in stratified fluids of the Earth's lower atmosphere ($0-50$ km). Such a coupling gives rise to the evolution of thermoacoustic internal gravity waves (IGWs), which are distinctive from the known IGWs in the literature. We perform numerical simulations to study the nonlinear interactions of velocity and density perturbations associated with the IGWs and thermal fluctuations corresponding to the thermal mode. We show that solitary vortices of IGWs coupled to the thermal wave can lead to thermoacoustic turbulence. We observe the formation of large-scale velocity potential flows and small-scale structures in the density and temperature profiles. Interestingly, while the wave energy spectra exhibit power laws: $ k_x^{-1.67}$ and $ k_z^{-2.89}$, respectively, for horizontal and vertical wave numbers, in the troposphere ($0-15$ km) with negative temperature gradient, the same in the stratosphere ($15-50$ km) with positive temperature gradient tend to relax toward $k_x^{-1.83}$-horizontal and $k_z^{-1.03}$-vertical spectra. We find that while the energy spectra in the tropospheric turbulence are consistent with the observed phenomena without temperature gradients, those in the stratosphere differ.

We present a minimal Path-Dependent Energy Lagrangian (PDEL) that generates, from a single action, the balance equations of mechanics and the entropy/heat equation for irreversible thermomechanical systems. The reversible part is the Helmholtz free energy, while irreversible effects enter through a history integral of channel powers. A single upper-limit/tangential variation rule makes the same instantaneous power appear as a dissipative force in the mechanical/internal-variable equations and as a positive source in the entropy/heat equation, closing the first law without double counting and guaranteeing nonnegative entropy production under mild monotonicity assumptions. PDEL preserves the classical Lagrangian mechanics while subsuming standard dissipative models (Kelvin–Voigt viscosity, diffusion) and their viscous heating, and clarifies the reversible character of thermo-mechanical cross terms. The formulation offers a compact alternative to Rayleigh/Onsager appendices and GENERIC/metriplectic brackets, with limited algebraic complexity and straightforward extension to multiphysics.

Breaking optical reciprocity enables new regimes of light–matter interaction with broad implications for fundamental physics and emerging quantum technologies. Although various approaches have been explored to achieve optical nonreciprocity, realizing it at the single-photon level has remained a major challenge. Here, we demonstrate nonmagnetic optical nonreciprocity – including both isolation and circulation – in the quantum regime, enabled by efficient and noiseless all-optical frequency conversion on an integrated III-V photonic chip. Our device preserves the quantum coherence and entanglement of the input photons while delivering exceptional performance parameters, including a high extinction ratio of 34 dB, low insertion loss of 0.8 dB, broad bandwidth of 44 GHz, high operational fidelity of 97%, and widely tunable operation wavelength. This realization of quantum optical nonreciprocity in a scalable photonic platform opens a pathway toward directional quantum communication and noise-resilient quantum networks.

Objective: This work presents a data-driven importance sampling-based variance reduction (VR) scheme inspired by active learning. The method is applied to the estimation of an optimal impact-parameter distribution in the calculation of ionization clusters around a gold nanoparticle (NP). Here, such an optimal importance distribution can not be inferred from principle. Approach: An iterative optimization procedure is set up that uses a Gaussian Process Sampler to propose optimal sampling distributions based on a loss function. The loss is constructed based on appropriate heuristics. The optimization code obtains estimates of the number of ionization clusters in shells around the NP by interfacing with a Geant4 simulation via a dedicated Transmission Control Protocol (TCP) interface. Main results: It is shown that the so-derived impact-parameter distribution easily outperforms the actual, uniform irradiation case. The results resemble those obtained with other VR schemes but do still slightly overestimate background contributions. Significance: While the method presented is a proof-of-principle, it provides a novel method of estimating importance distributions in ill-posed scenarios. The presented TCP interface described here is a simple and efficient method to expose compiled Geant4 code to other scripts, written for example, in Python.

Cell migration is a fundamental process underlying the survival and function of both unicellular and multicellular organisms. Crawling motility in eukaryotic cells arises from cyclic protrusion and retraction driven by the cytoskeleton, whose organization is regulated by reaction-diffusion (RD) dynamics of Rho GTPases between the cytosol and the cortex. These dynamics generate spatial membrane patterning and establish front-rear polarity through the coupling of biochemical signalling and mechanical feedback. We develop a cross-scale mean-field framework that integrates RD signalling with cytosolic and cortical hydrodynamics to capture emergent cellular locomotion. Our model reproduces diverse experimentally observed shape and motility phenotypes with small parameter changes, indicating that these behaviours correspond to self-organized limit cycles. Phase-space analysis reveals that coupling to both cytosolic flow and spatially varying surface tension is essential to recover the full spectrum of motility modes, providing a theoretical foundation for understanding amoeboid migration.

The hyperbolic model (HM) time integration scheme tackles parabolic problems by adding a small artificial second order time derivative term. Described by Samarskii in his 1971 book, the scheme reappeared as the generalized Du Fort-Frankel scheme in a 1976 paper by Gottlieb and Gustafsson. In this note we revisit accuracy and stability properties of the scheme. In particular, we show that the stability condition, formulated by Samarskii based on operator inequalities, coincides with the requirement that the eigenvalues of the amplification matrix (the stability function operator) are smaller than one in absolute value. However, under this condition, the norm of this matrix may exceed one and this, as recently pointed out by Corem and Ditkowski (2012), may corrupt convergence of the scheme. Hence, we also discuss whether this eventual stability lack can be detected and mitigated in practice.

Introduction: The inclusion of Breaking in the 2024 Paris Olympic Games introduced a distinctive competition format in which two athletes compete head-to-head in battle rounds. Unlike other aesthetic sports, where athletes receive independent scores for their performances, Breaking judges assess the relative performance quality between two competitors across five predefined evaluation aspects. This may considerably increase their cognitive load and warrants a thorough examination of judging reliability and potential national bias, the tendency of judges to favor athletes from their own country. Methods: Official scoring data from the 2024 Olympic Breaking events were analyzed. Interrater reliability was assessed using the Intraclass Correlation Coefficient, while national bias was estimated using mixed-effects modelling based on judges' individual scores. Results: The analyses revealed low to moderate interrater reliability in Breaking, notably lower than levels reported in other aesthetic sports. Moreover, substantial national bias was detected, with judges displaying a systematic tendency to favor athletes from their own country. Discussion: Although the observed national bias, despite its notable magnitude, likely had only a limited effect on final competition outcomes, the relatively low interrater reliability highlights potential weaknesses in the current scoring framework that may warrant refinement. Nonetheless, Breaking's unique system of aggregating judges' scores into discrete votes appears to reduce the influence of individual judges on competition outcomes, thereby enhancing the system's robustness against unilateral score manipulation.

We introduce a machine learning framework for moment-equation modeling of rarefied gas flows, addressing strongly non-equilibrium conditions inaccessible to conventional computational fluid dynamics. Our approach utilizes high-order moments and collision integrals, highly sensitive to non-equilibrium effects, as key predictive variables. Training datasets are created from one-dimensional steady shock simulations, and a methodology of computing collision integrals is developed. By learning thermodynamically consistent closures directly from DSMC data, our R13-ML model, combined with a discontinuous Galerkin solver for the transfer equations of moments, preserves physical structure and accurately predicts normal shock structures and generalizes to hypersonic and some unsteady, one-dimensional wave scenarios. This work bridges machine learning with continuum mechanics, offering a road map for high-fidelity aerothermal predictions in next-generation supersonic vehicles.

The inherent instability of oscillatory flows presents a significant challenge in microfluidics, impairing performance in different applications from particle detachemnt to organs-on-a-chip. Trapped air inside a microfluidic system passively dampens these fluctuations because of the compressible nature of air. However, a foundational theoretical model that describes this effect has remained elusive. Here, a first-principles model that fully characterizes the effects of a trapped air volume in oscillatory microfluidic flow is derived. The model identifies a dimensionless product as the governing parameter, unifying the interplay between air compressibility and fluidic resistance. It precisely predicts the volume displacement dynamics of the liquid front, which compared with the original flow, it presents amplitude reduction, phase shift, and transient drift. The theoretical framework was validated with different experiments across a broad range of conditions. This work transforms trapped air from a source of unpredictability into a powerful, predictable element for tailoring oscillatory flow stability, providing a rigorous design tool for microfluidic systems.

Under the dual background of deep adjustment of global energy pattern and severe challenges of environmental problems, solid oxide fuel cell (SOFC) has become the focus of research on efficient and clean energy conversion technology due to its many excellent characteristics. The electrochemical performance of SOFC is affected by various factors such as gas flow pattern (co-flow, counter-flow, cross-flow), flow rate (cathode and anode channel gases), and operating voltage. Accurately analysing the variation of electrochemical indexes with each factor is the basis for proposing the design scheme of high efficiency reaction of the cell. Therefore, a three-dimensional multi-field coupling model of SOFC is established in this study, and the model parameters and boundary conditions covering electrochemistry, gas flow, substance diffusion, etc. are set to study the influence of the coupling between factors on the electrochemical performance of the cell. These results show that with the decrease of operating voltage, the electrochemical reaction rate of the cell increases significantly, the gas mole fraction gradient increases, and the inhomogeneity of the electrolyte current density distribution is enhanced. Under low-voltage operating conditions, the cross-flow flow pattern shows better electrochemical performance advantages, and its power density profile takes the lead in different current density intervals. With the increase of the flow rate of the flow channel gas, the output power density curve of the cell shows an overall upward trend, and then the driving effect of the flow rate increase on the power density increase is gradually weakened due to the saturated cathodic reaction. This study reveals the influence of the coupling of flow pattern, flow rate and voltage on the electrochemical performance of SOFC, and provides guidance for the commercial application of SOFC.

Micro-electro-mechanical systems (MEMS)-based (chip-scale) alkali vapor cells are key components in emerging quantum technologies, where device performance critically depends on the atomic number density. Thus, it is important to have an accurate estimate of the atomic number density in MEMS-based alkali vapor cells to optimize light-matter interactions and design efficient quantum sensing systems. Here, a quantitatively validated method is presented for determining the rubidium (Rb) atomic number density in warm vapor using Single-Pass Absorption Spectroscopy (SPAS). The absolute transmission spectra are measured and modeled using the 780.24~nm and 420.29~nm transitions in Rb-filled MEMS vapor. The theoretical model employs a density-matrix formalism within the Lindblad framework and incorporates directly measurable experimental parameters, such as laser beam power, diameter, and cell temperature. The model explicitly accounts for optical pumping, Doppler broadening, and transit-time broadening effects and exhibits quantitative agreement ($> 99\%$) with experimental spectra over a broad range of temperatures (293-353~K), laser probe powers of approximately 10~$\mu$W-100~$\mu$W at the 780.24~nm transition and 8~$\mu$W-80~$\mu$W at the 420.29~nm transition, and cell lengths (2–100~mm). This method demonstrates a practical and reliable approach for determining the density of alkali vapor cells for quantum sensing, metrology, and quantum communication applications.

Scintillator-SiPM Particle Detectors (SSPDs) are compact, low-power devices with applications including particle physics, underground tomography, cosmic-ray studies, and space instrumentation. They are based on a prism-shaped scintillator with corner-mounted SiPMs. Previous work has demonstrated that analytic algorithms based on a physical model of light propagation can reconstruct particle impinging positions and tracks and estimate deposited energy and Linear Energy Transfer (LET) with moderate accuracy. In this study, we enhance this approach by applying machine learning (ML) methods, specifically gradient boosting techniques, to improve the accuracy of spatial location and energy deposition estimation. Using the GEANT4 simulation toolkit, we simulated cosmic muons and energetic oxygen ions traversing an SSPD, we trained XGBoost and LightGBM models to predict particle impinging positions and deposited energy. Both algorithms outperformed the analytic baseline. We further investigated hybrid strategies, including hybrid boosting and probing. While hybrid boosting provided no significant improvement, probing yielded measurable gains in both position and LET estimation. These results suggest that ML-driven reconstruction provides a powerful enhancement to SSPD performance.

Efficient lubrication is crucial for the performance and durability of high-speed gearboxes, particularly under varying load conditions. Excess lubrication leads to increased churning losses, while insufficient lubrication accelerates wear on contact surfaces. Due to the high rotational speeds involved, direct experimental visualization of lubricant distribution within gearboxes is challenging, making numerical simulations indispensable. Although various modelling approaches exist, a direct comparison that jointly evaluates accuracy and computational efficiency is missing. Furthermore, studies on the computational modelling of highly viscous lubricants such as grease in gearboxes are limited. This study addresses these gaps by comparing two mesh-based Eulerian solvers (OpenFOAM and Ansys-Fluent) and two Lagrangian particle-based solvers (PreonLab and MESHFREE) for oil distribution in gearboxes. Two benchmark cases are considered: one for qualitative assessment and another for quantitative evaluation. OpenFOAM and Ansys-Fluent show good agreement with the experiment data in selected cases, but incur a significant computational cost. PreonLab performs well qualitatively, yet exhibits greater deviation in quantitative predictions. These comparisons provide information for selecting the suitable solver according to specific simulation requirements. Furthermore, the study extends to grease distribution by first validating the solver and then investigating the influence of filling volume and gear speed on the amount of grease deposited on gears. The benchmark cases presented provide a reference framework for evaluating additional solvers in future gearbox lubrication studies.

Symbol decoding in multiple-input multiple-output (MIMO) wireless communication systems requires the deployment of fast, energy-efficient computing hardware deployable at the edge. The brute-force, exact maximum likelihood (ML) decoder, solved on conventional classical digital hardware, has exponential time complexity. Approximate classical solvers implemented on the same hardware have polynomial time complexity at the best. In this article, we design an alternative ring-oscillator-based coupled oscillator array to act as an oscillator Ising machine (OIM) and heuristically solve the ML-based MIMO detection problem. Complementary metal oxide semiconductor (CMOS) technology is used to design the ring oscillators, and ferroelectric field effect transistor (FeFET) technology is chosen as the coupling element (X) between the oscillators in this CMOS + X OIM design. For this purpose, we experimentally report high linear range of conductance variation (1 micro-S to 60 micro-S) in a FeFET device fabricated at 28 nm high-K/ metal gate (HKMG) CMOS technology node. We incorporate the conductance modulation characteristic in SPICE simulation of the ring oscillators connected in an all-to-all fashion through a crossbar array of these FeFET devices. We show that the above range of conductance variation of the FeFET device is suitable to obtain optimum OIM performance with no significant performance drop up to a MIMO size of 100 transmitting and 100 receiving antennas, thereby making FeFET a suitable device for this application. Our simulations and associated analysis using the Kuramoto model of oscillators also predict that this designed classical analog OIM, if implemented experimentally, will offer logarithmic scaling of computation time with MIMO size, thereby offering a huge improvement (in terms of computation speed) over aforementioned MIMO decoders run on conventional digital hardware.

Emergent quantum networks are the essential ingredient for securely connecting multiple users worldwide, extensively deployed in both fibre and free-space. An essential element is the multiplexing of entanglement to multiple users, overcoming the peer-to-peer restriction of quantum key distribution (QKD), so far successfully shown in fibre-based architectures. Here, we demonstrate a free-space quantum space division multiplexing architecture using just one entanglement source to realise a fully connected twelve-channel quantum network for seamless QKD connections between six users. The network achieves record coincidence rates exceeding $3 \times 10^{4}$ s$^{-1}$ between any pair of nodes on the network, for sifted key rate of over 400 kbps. Our approach overcomes the active switching hurdle that has hindered the free-space deployment of quantum multiplexing, is fully passive, easily scalable to more nodes and compatible with fibre-based integration, thus opening a new path to scalable and resource-efficient quantum networks that utilise free-space links.

Active matter, encompassing natural systems, converts surrounding energy to sustain autonomous motion, exhibiting unique non-equilibrium behaviors such as active turbulence and phase separation. In this study, we develop a continuum two-fluid model for a binary mixture of an active nematic and a passive Newtonian fluid, coupling Cahn-Hilliard dynamics for phase separation with Beris-Edwards nematohydrodynamics and two distinct momentum equations connected by viscous drag. A phase field-based lattice Boltzmann method is used to investigate the existence of active turbulence and phase separation in the binary mixture. We find that active stress enhances velocity and vorticity differences between phases, and that increased active concentration promotes stronger inter-fluid coupling. Activity not only amplifies turbulent fluctuations but also arrests domain coarsening, leading to a finite characteristic length scale that decreases with increasing activity. Key parameters, like active parameter, tumbling parameter and Frank elastic constant, affect the characteristic scale of flow. These results highlight the role of relative motion and drag-mediated momentum transfer in active binary mixtures, providing a framework for studying systems such as bacterial suspensions in polymeric fluids or active emulsions.

We use advanced computational techniques to study the electronic structure of the Hf$^{12+}$ ion, with the goal of assessing its potential for use in highly accurate atomic optical clocks and search for new physics. Such clocks should combine low sensitivity to external perturbations with high sensitivity to a possible time variation of the fine-structure constant $\alpha$. The system features two clock transitions. One is an $f-p$ transition in terms of single-electron states, which exhibits strong sensitivity to variations in $\alpha$. The other is an electric-quadrupole (E2) transition between states of the ground-state configuration, which can serve as an anchor transition for measuring one frequency against the other. All three relevant states possess very small and nearly equal static dipole polarizabilities, resulting in an extremely small blackbody-radiation shift. The quadrupole shift is also small and can be further suppressed. Altogether, Hf$^{12+}$ appears to be a highly promising candidate for both precision timekeeping and searches for new physics.

Perovskite materials, known for their structural versatility and multifunctional properties, continue to draw interest for advanced electronic and optoelectronic applications. In this study, we investigate the elastic and strain–engineered mechanical, electronic properties and optical properties of the orthorhombic La2AlGaO6 (LAGO) hybrid perovskite using first–principles quantum mechanical calculations based on density functional theory (DFT). Structural optimizations were performed using both the local density approximation (LDA) and the generalized gradient approximation (GGA). The mechanical stability of LAGO was confirmed through the Born–Huang criteria, and key elastic constants (C11, C12, C33, C44, and C66) were evaluated. These constants were further used to derive mechanical parameters such as Young's modulus, bulk modulus, shear modulus, Poisson's ratio, Cauchy's pressure, and anisotropic factor, offering insights into the material's ductility, hardness, and elastic anisotropy. Crucially, we explored the influence of biaxial strain on the electronic band structure, DOS/PDOS, and Fermi energy, revealing significant band gap modulation under compressive and tensile strain, and hence, varying the optical properties. The coupling between elastic response and electronic structure highlights LAGO's potential for tunable device applications, where mechanical stimuli can be employed to tailor its electronic functionality.

Physics-Informed Neural Networks (PINNs) offer a flexible framework for solving nonlinear partial differential equations (PDEs), yet conventional implementations often fail to preserve key physical invariants during long-term integration. This paper introduces a structure-preserving PINN framework for the nonlinear Korteweg–de Vries (KdV) equation, a prototypical model for nonlinear and dispersive wave propagation. The proposed method embeds the conservation of mass and Hamiltonian energy directly into the loss function, ensuring physically consistent and energy-stable evolution throughout training and prediction. Unlike standard tanh-based PINNs~\cite{raissi2019pinn,wang2022modifiedpinn}, our approach employs sinusoidal activation functions that enhance spectral expressiveness and accurately capture the oscillatory and dispersive nature of KdV solitons. Through representative case studies – including single-soliton propagation (shape-preserving translation), two-soliton interaction (elastic collision with phase shift), and cosine-pulse initialization (nonlinear dispersive breakup) – the model successfully reproduces hallmark behaviors of KdV dynamics while maintaining conserved invariants. Ablation studies demonstrate that combining invariant-constrained optimization with sinusoidal feature mappings accelerates convergence, improves long-term stability, and mitigates drift without multi-stage pretraining. These results highlight that computationally efficient, invariant-aware regularization coupled with sinusoidal representations yields robust, energy-consistent PINNs for Hamiltonian partial differential equations such as the KdV equation.

A systematic framework for realizing $\mathbb{Z}_2$ gauge extensions of hyperbolic lattices within the nearest-neighbor tight-binding formalism is developed. Using the triangle group $\Delta(2,8,8)$ as an example, we classify all inequivalent projective symmetry groups by computing the second cohomology group $H^2(\Delta(2,8,8),\mathbb{Z}_2)$. Each class corresponds to a distinct flux configuration and can be constructed by tight-binding models to verify the symmetry relations of the extended group. The translation subgroups of the $\mathbb{Z}_2$ extended lattices are associated with high genus surfaces, which follows the Riemann-Hurwitz formula. By applying the Abelian hyperbolic band theory, we find the all-flat dispersions along specific directions in momentum space and van Hove singularities correlated with discrete eigenenergies. Our results establish a general route to investigate gauge-extended hyperbolic lattices and provide a foundation for further studying symmetry fractionalization and spin liquid phases in non-Euclidean geometries.

The hybrid light-matter character of exciton-polaritons gives rise to distinct polariton parametric scattering (PPS) process, which holds promise for frontier applications in polaritonic quantum devices. However, the stable excitation and coherent optical manipulation of PPS remain challenging due to scattering bottlenecks and rapid dephasing effect in polariton many-body systems. In this study, we first report the direct observation and optical amplification of non-degenerate intermode PPS lasing at room temperature (RT). The specific polariton branch of strong-coupled nanobelt planar microcavity is resonantly excited by a near-infrared (NIR) femtosecond laser via two-photon absorption (TPA) scheme, and the non-degenerate signal- and idler-states are stimulated. Angle-resolved dispersion patterns clearly reveal the evolution of the pump-, signal-, and idler-states under different excitation powers. Based on our self-constructed ultrafast femtosecond resonant optical trigger set-up, a selective enhancement and modulation of the signal-state is realized. Furthermore, the dynamic measurements of nonlinear signal-state enhancement process demonstrate a sub-picosecond response time (0.4ps), confirming its potential for ultrafast optical manipulation. Our work establishes a platform for exploring TPA-driven PPS laser and provides a novel optical modulation route for polariton-based optoelectronic devices.

We venture that the long evolution of science may be viewed as unfolding over three blurred epochs. The first epoch spans the slow, haphazard, error-ridden realization of scientific truths along with foundational scientific methods. The second epoch covers the discovery that everything is formed from atoms and other building blocks. And in the age of computation, the third epoch affords the science of complex systems.

Ventricular Fibrillation (VF) is a malignant cardiac arrhythmia and the leading cause of sudden cardiac death, characterized by disorganized, high-frequency ventricular activity that results in the rapid loss of coordinated pump function and circulatory collapse. While the clinical manifestations of VF are well established, the multiscale mechanisms linking cellular electrophysiology to whole-organ mechanical failure remain challenging to study experimentally. Computational modeling therefore provides a critical platform for mechanistic investigation. This work presents a hierarchical computational study of VF beginning with the implementation and verification of a closed-loop, lumped-parameter (0D) hemodynamic model of the cardiovascular system. The verified model is used to quantify the global circulatory consequences of a prescribed VF state, demonstrating a 62.4% reduction in cardiac output and highlighting the dominant role of impaired ventricular filling and contractile failure. Recognizing the limitations of prescribing arrhythmia dynamics, we then propose a pathway toward an integrated, multiscale framework coupling the 0D hemodynamic core with models of cardiac electrophysiology and autonomic regulation to enable simulation of emergent arrhythmogenic behavior and reflex responses. Finally, we introduce an interactive simulator derived from the verified 0D model, designed to support education, hypothesis testing, and future integration of multiscale components. This work establishes a mechanistic baseline and software foundation for next-generation computational studies of VF and cardiovascular control.

The ZaP-HD sheared-flow-stabilized (SFS) Z-pinch device is a testbed for experimental investigation of plasma-electrode interactions. The graphite electrode is exposed to a high temperature, high density Z-pinch plasma while supplying large pinch currents. In-situ measurements of the gross carbon erosion flux obtained with S/XB spectroscopy exceed the expected flux from physical sputtering, but have reasonable agreement with the expected sublimation flux. Comparison of the ionization mean free paths of neutrals produced through both erosion processes shows that sublimated carbon is ionized within the sheath while sputtered carbon is ionized beyond the sheath. This suggests a process of electrode recycling and self-healing through redeposition. The sputtered carbon is primarily responsible for net erosion. Ex-situ analysis of electrode material is enabled by the design of a removable coupon. Three different plasma exposure conditions varied the pinch current and number of pulses. Net mass loss measurements support the physical picture of electrode recycling. Erosion rates range from 0.01 to 0.1 mg/C, which are comparable to existing arc discharge devices. Measurements of the microscopic surface morphology and roughness reveal irregular consolidated structures and general smoothing except at high particle fluence. Crack formation suggests the importance of repetitive thermal cycles. Definitive features of sputtering such as pitting and cratering are absent, although further study is needed to attribute the observed changes to other processes. These results indicate some alignment with erosion processes in high-powered arc discharges, which successfully operate solid electrodes in extreme environments. This provides confidence in managing electrode erosion in the SFS Z-pinch configuration.

This work studies the effectiveness of acoustic black holes to generate broadband non-reflective traveling waves using a single excitation source. This is inspired by similar observations in the basilar membrane of the mammalian inner ear. An aluminum beam is machined to introduce a gradual, asymmetric power-law taper at one of its ends. This tapered termination was then partially covered by viscoelastic tape to enhance the acoustic black hole effect in the system. This setup is then used to validate a model developed based on the Euler-Bernoulli beam theory. Following good agreement between the model and the experimental setup over a broad excitation frequency range (1 kHz to 10 kHz), the model is used to conduct a parametric study investigating the effects of different variables on the system's response. This study revealed the effectiveness of acoustic black holes in sustaining traveling waves over a broad frequency range. By optimizing parameters, such as the power-law order, one can significantly enhance this effect. This is especially noticeable towards the lower-frequency end.