Papers

We propose a microlocal-Riemannian framework for the three-dimensional incompressible Navier-Stokes equations on a smooth oriented Riemannian manifold (M,g). The dynamics is lifted to the unit cosphere bundle S*M via a normal-coordinate microlocal transform whose construction is justified by the positive homogeneity of the principal symbol of the linearised system in the cotangent fiber variable. Once the velocity field is fixed, the lifted dynamics is a linear non-autonomous transport-dissipation equation on a compact phase space; its coefficients encode intrinsic geometric quantities of the original flow. We introduce a microlocal energy, an angular volume functional and a directional entropy, and analyse their dissipation along the lifted dynamics. An effective affine connection encodes the back-reaction of the velocity gradient on the geometry of S*M and gives rise to a Ricci-type microlocal evolution. The framework yields a sharp geometric equivalence: a smooth solution fails to extend past time T if and only if at least one of three intrinsic microlocal controls – deformation integrability, directional-entropy boundedness, or lifted-energy boundedness – fails. A dimensional analysis exhibits a symmetry-lock phenomenon, in which the asymptotic vanishing of the volume of fibers enforces angular isotropy and topologically obstructs the formation of microlocal angular singularities. The framework is illustrated explicitly on the flat torus, where every assumption is verified, and is extended to the Euclidean setting under a uniform-coordinate hypothesis. The global regularity problem is not resolved here; rather, it is recast as a structural-stability question for a compact, symmetry-constrained, microlocally coercive evolution system, with the role of viscosity made explicit through spectral coercivity.

Silicate bioceramics have attracted significant attention in medical applications, particularly in hard tissue regeneration, because of their controllable chemical, physical, and biological functionalities, while ensuring biocompatibility. The coordination of silicate bioceramics with mono-dopants has been extensively studied to enhance their osteogenesis, angiogenesis, and antibacterial activity. However, the concept of employing dual or multiple co-doping to further enhance these biomaterials still demands more attention. This review paper originally focuses on the effect of chemical interactions among co-dopants and the principal constituents of the silicates, on the bio-behavior of these bioceramics. Additionally, future prospects of co-doped silicate bioceramics are outlined, including in vivo studies, clinical trials, and potential commercialization.

We present a systematic framework of indices designed to characterize Large Language Model (LLM) responses when challenged with rebuttals during a chat. Assessing how LLMs respond to user dissent is crucial for understanding their reliability and behavior patterns, yet the complexity of human-LLM interactions makes systematic evaluation challenging. Our approach employs a fictitious-response rebuttal method that quantifies LLM behavior when presented with multiple-choice questions followed by deliberate challenges to their fictitious previous response. The indices are specifically designed to detect and measure what could be characterized as sycophantic behavior (excessive agreement with user challenges) or stubborn responses (rigid adherence to the fictitious response in the chat history) from LLMs. These metrics allow investigation of the relationships between sycophancy, stubbornness, and the model's actual mastery of the subject matter. We demonstrate the utility of these indices using two physics problems as test scenarios with various OpenAI models. The framework is intentionally generalizable to any multiple-choice format question, including on topics without universally accepted correct answers. Our results reveal measurable differences across OpenAI model generations, with trends indicating that newer models and those employing greater "Reasoning Effort" exhibit reduced sycophantic behavior. The FR pairing method combined with our proposed indices provides a practical, adaptable toolkit for systematically comparing LLM dialogue behaviors across different models and contexts.

We present an AI-driven approach to discover compounds with optimal properties for CO2 capture from flue gas-refinery emissions' primary source. Focusing on ionic liquids (ILs) as alternatives to traditional amine-based solvents, we successfully identify new IL candidates with high working capacity, manageable viscosity, favorable regeneration energy, and viable synthetic routes. Our approach follows a five-stage pipeline. First, we generate IL candidates by pairing available cation and anion molecules, then predict temperature- and pressure-dependent CO2 solubility and viscosity using a GNN-based molecular property prediction model. Next, we convert solubility to working capacity and regeneration energy via Van't Hoff modeling, and then find the best set of candidates using Pareto optimization, before finally filtering those based on feasible synthesis routes. We identify 36 feasible candidates that could enable 5-10% OPEX savings and up to 10% CAPEX reductions through lower regeneration energy requirements and reduced corrosivity-offering a novel carbon-capture strategy for refineries moving forward.

Reliable temperature forecasting in Enhanced Geothermal Systems (EGS) is essential, yet petroleum-based decline curves and many machine-learning surrogates do not enforce geothermal heat transfer, while thermo-hydro-mechanical (THM) simulation remains computationally expensive. This study proposes a physics-consistent framework that advances both decline-curve analysis and surrogate modeling. The classical Arps decline family is generalized for geothermal use by introducing an equilibrium-temperature term motivated by Newton-type cooling, ensuring finite late-time temperature limits while reducing exactly to the conventional Arps forms when the equilibrium term is set to zero. The extended decline curves are validated against Utah FORGE downhole temperature measurements and then used to construct learning surrogates on a controlled THM dataset spanning fracture count, well spacing, fracture spacing, host-rock thermal conductivity, and circulation rate. An equation-informed neural network embeds the modified decline equations as differentiable internal computational layers to produce full 0-60 month temperature trajectories from design and operational inputs. A probabilistic Gaussian Process Regression surrogate is also developed for direct multi-horizon forecasting with calibrated uncertainty, while a direct XGBoost regression baseline provides a purely data-driven reference. Across the simulation dataset, the extended decline models reproduce temperature trajectories with near-perfect fidelity (median RMSE = 0.071 {\deg}C), and the equation-informed network achieves typical hold-out errors of MAE = 3.06 {\deg}C and RMSE = 4.49 {\deg}C. The Gaussian Process surrogate delivers the strongest predictive accuracy across 3-60 month horizons (RMSE = 3.39 {\deg}C; MAE = 2.34 {\deg}C) with well-calibrated uncertainty, whereas the XGBoost baseline exhibits higher errors.

We investigate a unified dark-energy scenario based on the combined effects of Barrow entropy corrections and the QCD ghost mechanism, referred to as the BH–QCDGDE model. The dark-energy density is constructed in a generalized holographic form that incorporates both Barrow-deformed entropy corrections and low-energy QCD vacuum effects within a single framework. The cosmological dynamics are analyzed in a spatially flat Friedmann–Lemaître–Robertson–Walker background. The model exhibits a smooth transition from a decelerated matter-dominated era to a late-time accelerated phase without crossing the phantom divide, indicating a viable background evolution. An equivalent scalar-field description of the effective dark-energy sector is reconstructed and shown to admit a quintessence-like behavior. The thermodynamic viability is examined by testing the generalized second law at the apparent horizon, which is found to be satisfied throughout the parameter space. The classical stability of the model is further investigated through the squared speed of sound, revealing the role of model parameters in shaping stable cosmological regimes. Overall, the BH–QCDGDE framework provides a consistent and physically viable description of late-time cosmic acceleration.

Interferometry techniques are essential for extracting phase information from optical systems enabling precise measurements of dispersion and highly sensitive detection of perturbations. While phase sensing offers enhanced sensitivity compared to conventional spectroscopy methods, this sensitivity often makes systems more vulnerable to external factors such as vibrations, introducing instability and noise. In this work, we demonstrate a broadband and AI-enhanced interferometry method, denoted general polarization common-path interferometry (GPCPI) that relaxes the polarization constraints of traditional common-path interferometry. The polarization decoupling feature enables simultaneous amplitude and phase measurements supplemented with deep neural autoencoders to detect phase anomalies in the spectrum through the analysis of second order derivative mapping of the phase profile, enhancing the accuracy of broadband phase measurements. The approach enables an order of magnitude improvement in phase stability compared to state-of-the-art interferometry techniques, leading to higher accuracy in phase sensing. Plasmonic metasurface phase sensing and hyperspectral single-cell dispersion imaging demonstrate the capability and sensitivity of the method over conventional spectroscopy. Our adopted version of deep learning model, ConvNeXt V2, enables real-time tracking of phase variation with minimized noise. Interference fringes affected by the cell-cultured samples reveal the fingerprints of the normal (CCD-32Sk) vs cancerous (COLO-829) skin cells, enabling cell classification and disease diagnosis at single-cell level through hyperspectral dispersion imaging. The proposed technique offers a reliable, compact, and stable solution for broadband phase measurements and single-cell dispersion imaging for applications in metrology, molecular diagnostics, drug discovery, and quantum sensing.

We present neutrino-transport algorithms implemented in the toolkit for high-order neutrino-radiation hydrodynamics (thornado) and their coupling to self-gravitating hydrodynamics within the adaptive mesh refinement (AMR)-based multiphysics simulation framework Flash-X. thornado, developed primarily for simulations of core-collapse supernovae (CCSNe), employs a spectral, six-species two-moment formulation with algebraic closure and special-relativistic observer corrections accurate to $O(v/c)$, and uses discontinuous Galerkin (DG) methods for phase-space discretization combined with implicit-explicit time stepping. A key development is a nonlinear neutrino-matter coupling algorithm based on nested fixed-point iteration with Anderson acceleration, enabling fully implicit treatment of collisional processes, including energy-coupling interactions such as neutrino-electron scattering and pair production. Coupling to finite-volume (FV) hydrodynamics is achieved with a hybrid DG-FV representation of the fluid variables and operator-split evolution in Flash-X. The implementation is verified using basic transport tests with idealized opacities and relaxation and deleptonization problems with tabulated microphysics. Spherically symmetric CCSN simulations demonstrate accuracy and robustness of the coupled scheme, including close agreement with the CCSN simulation code Chimera. An axisymmetric CCSN simulation further demonstrates the viability of DG-based neutrino transport for multidimensional supernova modeling within Flash-X. thornado's neutrino-transport solver is GPU-enabled using OpenMP offloading or OpenACC, and all CCSN applications included in this work use the GPU implementation. Together, these results establish a foundation for future enhancements in physics fidelity, numerical algorithms, and computational performance, for increasingly realistic large-scale CCSN simulations.

Subatomic particles can interact with target nuclei in matter or decay in flight, and an individual high-energy particle can induce a particle shower composed of numerous, lower-energy secondaries. These particle showers broadly exhibit universality across diverse media, including air, water, ice, and other materials, with their development governed by the Standard Model. Full Monte Carlo simulation of particle showers, where each secondary is individually tracked and propagated, can be a computational challenge to perform at scale. Experiments thus resort to parametrized approximations when efficient simulation becomes necessary. Here, we construct distributions of parameters capable of describing the Cherenkov light yield from particle showers in ice or water. Sampling from the distributions allows for a much improved description of event-to-event fluctuations, in amplitude and shape, along the shower axis. Including these effects is essential for a more accurate simulation of signal and background events in current and next-generation neutrino telescopes.

Background Objectives:The biomechanics of Tori s body motion during Sutemi,sacrifice techniques,Makikomi wrapping techniques,and Tai Atari,Body Strike techniques in judo is crucial for understanding these techniques' effectiveness and safety. This study examined the biomechanics by analysing the entire body of the tori during execution. Methods:We analysed Tori s body motion using original biomechanical models, considering the balance of sacrificing to throw Uke and body mass to strike the opponent. General equations for Sutemi, Makikomi, and Tai atari were established, and their utilisation in high level male competition was statistically analysed. Results:The results show that effective Sutemi and Makikomi execution depends on the correct use of Tori's body mass.The biomechanical model revealed the relationships between angular falling speed, body angle, and athlete height.The model allowed us to evaluate the falling velocity, showing that the angular velocity and acceleration were proportional to the gravitational acceleration and body-Tatami angle and inversely proportional to the athlete height.The stopping and sliding forces are directly proportional to the sum of the Tori and Uke masses and the body Tatami angle but are independent of the athletes eight. Tai Atari s force was quantified to evaluate its situational applicability in competition. Conclusion:This study advanced the biomechanical understanding of Sutemi, Makikomi, and Tai Atari using two original theoretical models, highlighting the importance of Tori s body mass motion. The findings obtained can influence training and competition strategies, enhance technique effectiveness, and reduce injury risks.

Spontaneous symmetry breaking leads to diverse phenomena across the natural sciences, from the Higgs mechanism in particle physics to superconductors and collective animal behavior. In photonic systems, the symmetry of light states can be broken when two optical fields interact through the Kerr nonlinearity, as shown in early demonstrations with counterpropagating and cross-polarized modes. Here, we report the first observation of color symmetry breaking in an integrated silicon nitride microring, where spontaneous power imbalance arises between optical mode at different wavelengths, mediated by the Kerr effect. The threshold power for this effect is as low as 19 mW. By examining the system's homogeneous states, we further demonstrate a Kerr-based nonlinear activation-function generator that produces sigmoid-, quadratic-, and leaky-ReLU-like responses. These findings reveal previously unexplored nonlinear dynamics in dual-pumped Kerr resonators and establish new pathways towards compact, all-optical neuromorphic circuits.

Extraordinary acoustic transmission is commonly associated with periodic or multi-aperture structures. In this work, we show that a single subwavelength slit can support strongly enhanced transmission when its boundary response is described by an effective impedance. Using a reduced analytical model together with numerical calculations, we demonstrate that appropriate impedance tuning leads to efficient coupling between the incident field and the slit mode, resulting in transmission levels approaching unity. The observed enhancement is governed by impedance matching rather than geometric periodicity, highlighting a minimal mechanism for extraordinary transmission. This study establishes boundary impedance control as a versatile route for manipulating acoustic wave transport through deeply subwavelength apertures.

Atmospheric blocking events are quasi-stationary high-pressure systems that disrupt the typical paths of polar and subtropical air currents, often producing prolonged extreme weather events such as summer heat waves or winter cold spells. Despite their critical role in shaping mid-latitude weather, accurately modeling and analyzing blocking events in long meteorological records remains a significant challenge. To address this challenge, we present an uncertainty visualization framework for detecting and characterizing atmospheric blocking events. First, we introduce a geometry-based detection and tracking method, evaluated on both pre-industrial climate model simulations (UKESM) and reanalysis data (ERA5), which represent historical Earth observations assimilated from satellite and station measurements onto regular numerical grids using weather models. Second, we propose a suite of uncertainty-aware summaries: contour boxplots that capture representative boundaries and their variability, frequency heatmaps that encode occurrences, and 3D temporal stacks that situate these patterns in time. Third, we demonstrate our framework in a case study of the 2003 European heatwave, mapping the spatiotemporal occurrences of blocking events using these summaries. Collectively, these uncertainty visualizations reveal where blocking events are most likely to occur and how their spatial footprints evolve over time. We envision our framework as a valuable tool for climate scientists and meteorologists: by analyzing how blocking frequency, duration, and intensity vary across regions and climate scenarios, it supports both the study of historical blocking events and the assessment of scenario-dependent climate risks associated with changes in extreme weather linked to blocking.

The outbreak of mutant strains and vaccination behaviors have been the focus of recent epidemiological research, but most existing epidemic models failed to simultaneously capture viral mutation and consider the complexity and behavioral dynamics of vaccination. To address this gap, we develop an extended SIRS model that distinguishes infections with the original strain and a mutant strain, and explicitly introduces a vaccinated compartment state. At the behavioral level, we employ evolutionary game theory to model individual vaccination decisions, where strategies are determined by both neighbors' choices and the current epidemiological situation. This process corresponds to the time-varying vaccination rate of susceptible individuals transitioning to vaccinated individuals at the epidemic spreading level. We then couple the epidemic and vaccination behavioral processes through the microscopic Markov chain approach (MMCA) and finally investigate the evolutionary dynamics via numerical simulations. The results show that our framework can effectively mitigate outbreaks across different disease scenarios. Sensitivity analysis further reveals that vaccination uptake is most strongly influenced by vaccine cost, efficacy, and perceived risk of side effects. Overall, this behavior-aware modeling framework captures the co-evolution of viral mutation and vaccination behavior, providing quantitative and theoretical support for designing effective public health vaccination policies.

This perspective explores the evolution of materials informatics, from its foundational roots in physics and information theory to its maturation through artificial intelligence (AI). We trace the field's trajectory from early milestones to the transformative impact of the Materials Genome Initiative and the recent advent of large language models (LLMs). Rather than a mere toolkit, we present materials informatics as an evolving ecosystem, reviewing key methodologies such as Bayesian Optimization, Reinforcement Learning, and Transformers that drive inverse design and autonomous self-driving laboratories. We specifically address the practical challenges of LLM integration, comparing specialist versus generalist models and discussing solutions for uncertainty quantification. Looking forward, we assess the transition of AI from a predictive tool to a collaborative research partner. By leveraging active learning and retrieval-augmented generation (RAG), the field is moving toward a new era of autonomous materials science, increasingly characterized by "human-out-of-the-loop" discovery processes.

We compute the thermodynamic phase diagram of seventeen elemental metals with hexagonal close-packed (hcp), face-centered cubic (fcc), and body-centered cubic (bcc) crystal structures using finite-temperature density functional theory. Helmholtz free-energy differences between competing hcp, fcc, and bcc phases are evaluated as functions of electronic temperature up to 7 eV, allowing us to identify solid-solid phase transitions driven by electronic entropy. The systems studied include Zr, Ti, Cd, Zn, Co, and Mg (hcp), Ni, Cu, Ag, Al, Pt, and Pb (fcc), and Cr, W, V, Nb, and Mo (bcc) in their ground-state structures. From the free-energy crossings, we extract the transition electronic temperatures and analyze systematic trends across the metallic systems. We found that all the studied systems go through one or two solid-solid phase transition caused purely by electronic entropy except Mg and Pb. Our results establish electronic entropy as a key factor governing structural stability in metals under strong electronic excitation.

We present a high-order implicit large eddy simulation (ILES) approach for simulating flows at the nearly incompressible regime. Our methodology based on utilization of a nodal discontinuous Galerkin (DG) discretization of the Boltzmann equations. The compactness and low-dissipative nature of the discontinuous Galerkin method are leveraged to mimic traditional large eddy simulations with subgrid-scale models. One of the key requirements of ILES is to provide dissipation only within a narrow band of high wavenumbers. This is validated through numerical experiments on the Taylor-Green Vortex problem in detail at a Reynolds number where varying scales of coherent turbulent structures are present. Furthermore, the approach is validated for external aerodynamic configurations by simulating the flow over a sphere at a Reynolds number of $Re=3700$, capturing the laminar-turbulent transition and the complex multiscale vortex dynamics characteristic of this regime. The results demonstrate the capability of the high-order DG-Boltzmann formulation to accurately capture transitional and turbulent flow features without the use of explicit sub-grid scale modeling, highlighting its potential as a robust and physically consistent framework for ILES of nearly incompressible turbulent flows.

We investigate the solidification of a water rivulet flowing over a cold inclined substrate and the resulting formation of three-dimensional ice structures. Using a controlled hydraulic and thermal setup, combined with spatiotemporal phase-shifting profilometry and infrared thermography, we characterize both the transient evolution and the final morphology of the ice. We show that a typical experiment proceeds through three stages: formation of a straight ice ridge that stabilizes the rivulet, destabilization and lateral excursions of the flow leading to rapid transverse spreading of the ice structure, and progressive thickening and smoothing of the ice block. Across a wide range of flow rates, inclinations and thermal conditions, the final morphology comprises an upstream triangular lateral envelope, followed by a downstream region of nearly constant width once the substrate edges are reached. Infrared measurements reveal that the rivulet residence time on the substrate follows a Gaussian distribution in the azimuthal angle, implying that the central region of the structure is visited far more frequently than its lateral edges. Focusing on the domain where the height has converged at the end of the experiments, we develop a two-dimensional theoretical model that couples a hydrodynamic model for the rivulet geometry with heat transport in both liquid and solid phases. In the large Péclet number limit, the model predicts an exponential increase of the stationary ice height along the flow direction and it shows an excellent agreement with the experimental height field. We further show how the combination of the stationary height profile and the non-uniform residence time distribution controls the angular convergence of the ice cross-sections.

We propose a new two-fluid model for a partially ionized magnetoplasma under gravity, in which electrons and neutrals are treated as a single fluid, while singly charged positive ions are a separate fluid. We observe that the classical result of gravitational instability (also known as Rayleigh-Taylor instability) in fully ionized plasmas becomes significantly modified by the influence of ion-neutral collisions (with frequency $\nu_{\rm{in}}$) and transverse wave numbers ($k_x$ and $k_y$). The instability growth rate can be enhanced or decreased depending on the values of the ratios $\kappa\equiv k_x/k_y$ and $f\equiv\nu_{\rm{in}}/\Omega_{\rm{ci}}$, where $\Omega_{\rm{ci}}$ is the ion-cyclotron frequency. We also estimate the growth rates relevant to the ionospheric E-region and solar atmosphere, noting that such growth rates can have a maximum for $\kappa,~f\ll1$, or for $\kappa>1$ and $f\sim0.64$, and minimized for $f\gg1$ irrespective of the value of $\kappa$. Furthermore, the timescale of instability ranges from $1$ minute to $2$ minutes in the solar atmosphere, while in the E region, it ranges from $1$ minute to $80$ minutes. The latter can be a satisfactory result for the reported lifetime of solar prominence threads.

Carbon mineralization in basaltic rocks may offer rapid, permanent \ce{CO2} storage, yet fundamental controls on reactive transport and precipitation patterns remain poorly understood. This study integrates flow-through experiments at 80\degree C using \ce{CO2}-acidified seawater with geochemical simulation and multi-scale pore imaging to elucidate mineralization dynamics in basaltic glass. Results reveal that carbonate precipitation is nucleation-controlled and stochastic rather than growth-controlled and deterministic, with isolated accumulations forming randomly despite continuous supersaturation. Residence time exerts primary control: reducing flow rate from 0.05 to 0.005\,mL/min proved necessary for visible precipitation. Post-experiment analyses identified calcium carbonate and smectite phases. Multi-scale characterization of three basalt facies revealed that connected porosity fractions (1.3–32%) differ significantly from total porosity (18–42%), demonstrating that network topology controls permeability. Micro-CT analysis revealed that pore coordination numbers in basalts (modal = 2) were notably lower than those in reservoir sandstones, creating serial flow paths that are vulnerable to catastrophic permeability loss from modest precipitation. Precipitation-induced clogging scenarios were proposed, where distributed small precipitates cause more severe permeability degradation than large accumulations. The use of seawater complicates geochemistry and reduces mineralization efficiency compared to freshwater. Findings emphasize the need for probabilistic reactive transport modeling frameworks and realistic pore topologies, which are fundamentally different from conventional CCS operations.

Accurate information on the distance dependence of resonance energy transfer (RET) is crucial for its utilization as a spectroscopic ruler \re{of} nanometer scale distances. In this regard, understanding the effects of donor-acceptor quantum coherence and non-Markovian bath, which become significant at short distances, has significant implications. The present work investigates this issue theoretically by comparing results from a theory of coherent RET (CRET) with a nonequilibrium version of Förster's RET (FRET) theory, both accounting for non-Markovian bath effects. Even for a model where the donor-acceptor electronic coupling is of transition dipole interaction form, it is shown that the RET rate in general deviates from the inverse sixth power distance dependence as opposed to the prediction of the original FRET. It is shown that the donor-acceptor quantum coherence makes the \re{distance} dependence steeper than the sixth power although detailed manner of enhancement is sensitive to specific values of parameters. On the other hand, the non-Markovian bath effects make the \re{distance} dependence more moderate than the sixth power for both CRET and nonueqilibrium FRET because finite time scale of the bath causes the rate to be smaller than the prediction of original FRET. While these effects are \re{demonstrated clearly} in the population dynamics at sub-picosecond time scales, their contributions to the conventional RET efficiency are relatively minor. This indicates that the actual detection of such effects through conventional RET efficiency measurement requires either high precision or utilization of a donor with fast spontaneous decay rate of excitation.

A defining quantity of a physical system is its energy which is represented by the Hamiltonian. In closed quantum mechanical or/and coherent wave-based systems the Hamiltonian is introduced as a Hermitian operator which ensures real energy spectrum and secures the decomposition of any state over a complete basis set spanning the space where the states live. Pseudo-Hermitian, or PT symmetric, systems are a special class of non-Hermitian ones. They describe open systems but may still have real energy spectrum. The eigenmodes are however not orthogonal in general. This qualitative difference to Hermitian physics has a range of consequences for the physical behaviour of the system in the steady state or when it is subjected to external perturbations. This overview reviews the recent progress in the field of pseudo-Hermitian physics as it unfolds when applied to low-energy excitations of magnetically ordered materials. The focus is mainly on long wave length spin excitations (spin waves) with magnons being the energy quanta of these excitations. Various setups including ferromagnetic, antiferromagnetic, magnonic crystals, and hybride structures with different types of coupling to the environments as well as spatio-temporally engineered systems will be discussed with a focus on the particular aspects that are brought about by the pseudo-Hermiticity such as mode amplifications, non-reciprocal propagation, magnon cloaking, non-Hermitian skin effect, PT-symmetric assisted Floquet engineering, topological energy transfer, and field-induced enhanced sensitivity.

A novel design on miniaturized low-pass filters from artificial transmission line structures is proposed. Reactance components, i.e. capacitors and inductors, are directly constructed from microstrip structures based on electromagnetic simulation and parameter extraction. The constructed reactance components are integrated to build low-pass filters. The results of two 5th-order Butterworth and 0.5 dB ripple Chebyshev low-pass filters, which are realized by the proposed method, are presented. The proposed low-pass filter is compact and its size is only 23% of a conventional filter. The simulation agrees well to the measurements, which shows the validation of the proposed design method. The miniaturized low-pass filters have good applications in microwave systems.

The inclusion of semiconducting material within a composite barrier enables the perfectly uniform propagation of surface ionization waves (SIW) in air at atmospheric pressure regardless of the polarity of the applied electric field, unlike surface discharges generated using purely dielectric barriers. We exploit the photonic properties of silicon to stimulate the SIW using external irradiation by a 2-ns pulsed laser at 532 nm, with a fluence of 1.3 mJ/cm$^2$ per pulse at the surface. No effect is observed when irradiation occurs more than 3 $\mu$s before plasma generation. This timescale is attributed to the ambipolar diffusion of photoexcited carriers away from the Si-SiO$_2$ interface. When this delay shortens to less than 3 $\mu$s, the SIW propagates farther and with more intense optical emission. Furthermore, the energy of the discharge increases by up to 7%. The sensitivity to the laser-plasma delay demonstrates that the observed stimulation of the SIW cannot be due to the desorption of surface charge by irradiation.

Whole-brain radiotherapy (WBRT) is a common treatment due to its simplicity and effectiveness. While automated Field-in-Field (Auto-FiF) functions assist WBRT planning in modern treatment planning systems, it still requires manual approaches for optimal plan generation including patient-specific hyperparameters definition and plan refinement based on quality feedback. This study introduces an automated WBRT planning pipeline that integrates a deep learning (DL) Hyperparameter Prediction model for patient-specific parameter generation and a large-language model (LLM)-based conversational interface for interactive plan refinement. The Hyperparameter Prediction module was trained on 55 WBRT cases using geometric features of clinical target volume (CTV) and organs at risk (OARs) to determine optimal Auto-FiF settings in RayStation treatment planning system. Plans were generated under predicted hyperparameters. For cases in which the generated plan was suboptimal, quality feedback via voice input was captured by a Conversation module, transcribed using Whisper, and interpreted by GPT-4o to adjust planning settings. Plan quality was evaluated in 15 independent cases using clinical metrics and expert review, and model explainability was supported through analysis of feature importance. Fourteen of 15 DL-generated plans were clinically acceptable. Normalized to identical CTV D95% as the clinical plans, the DL-generated and clinical plans showed no statistically significant differences in doses to the eyes, lenses, or CTV dose metrics D1% and D99%. The DL-based planning required under 1 minute of computation and achieved total workflow execution in approximately 7 minutes with a single mouse click, compared to 15 minutes for manual planning. In cases requiring adjustment, the Conversational module successfully improved dose conformity and hotspot reduction.

Achieving robust single-polarization guidance in hollow-core fibers has remained a longstanding challenge, limiting their integration into precision photonic systems. Here, we report the first experimental realization of a low-loss, polarization filtering antiresonant hollow-core fiber (AR-HCF). Conventional AR-HCFs inherently support degenerate orthogonal polarization modes, making them vulnerable to polarization drift under environmental perturbations. Our dual-ring fiber design introduces polarization-selective resonant coupling to lossy cladding modes, enabling strong polarization filtering without compromising transmission efficiency. The fiber achieves a polarization extinction ratio exceeding 21 dB and a propagation loss as low as 0.15 dB m^-1 over a 10 m fiber length. The design is scalable across wavelength bands and maintains polarization discrimination under mechanical bending, making it highly suitable for applications in fiber-based gyroscopes, quantum optics, and polarization-sensitive nonlinear interactions. This work represents a significant step toward monolithic, polarization-selective hollow-core fiber systems.

Nonlocal metamaterials (MTMs) have recently attracted significant attention across different areas of wave physics, owing to their ability to translate long-range interactions among meta-atoms into a wide array of wavevector-dependent responses and functionalities. In this work, we introduce nonlocal transmission line metamaterials (TL MTMs) as a versatile platform to investigate and engineer nonlocality in the microwave frequency regime. We first establish a concise theoretical framework for nonlocal TL MTMs based on circuit and network theory, from which we derive the general dispersion relation for TL MTMs with arbitrarily complex nonlocal coupling configurations. Building upon this foundation, we demonstrate how such structures can be used to synthesize nearly arbitrary even-symmetric dispersion relations, effectively linking nonlocal circuit parameters to prescribed dispersion profiles. We then introduce time-switched nonlocal TL MTMs, a new class of metamaterials with time-varying nonlocality in which the nonlocal branches are dynamically activated as an electromagnetic pulse propagates through the structure. This platform enables complex, nearly arbitrary frequency-momentum transformations on a propagating pulse, as well as the excitation of modes with positive, negative, and zero group velocity. Finally, we experimentally validate our theoretical and numerical predictions with a proof-of-concept demonstration of a time-switched nonlocal TL MTM, observing a vertical transition in the dispersion diagram induced by abrupt time-switching. Our results provide new physical insights into the behavior of nonlocal MTMs, establish a versatile platform to investigate the interplay of frequency dispersion, spatial dispersion and time modulation, and, more broadly, lay a general foundation for the design of more advanced nonlocal and time-varying electromagnetic and photonic systems.

This paper aims at providing an accessible introduction to ultracold quantum gravimeters tailored for geophysicists. We do not focus here on geophysical applications, as these are already well known to geophysicists, but rather provide a pedagogical exposition of the quantum-mechanical concepts needed to understand the operation of quantum gravimeters. We present a review of gravimeters based on two- and three-level atomic systems, focusing on the fundamental mechanisms of atomic interferometry. The functioning of Mach-Zehnder interferometers is discussed through the action of $\pi/2$ and $\pi$ pulses, showing how the resulting phase shift encodes gravitational acceleration. The effect of noise is briefly discussed.

We present the final configuration chosen to be build for the Columbia Stellarator eXperiment (CSX), a new stellartor experiment at Columbia University. In a recent publication, Baillod et al. (NF, 2025) discussed in detail the different objectives, constraints, and optimization algorithms used to find an optimal configuration for CSX. In this paper, we build upon this first publication and find a configuration that satisfies all the constraints. We describe this final configuration including discussion of the coil finite build effects, sensitivity analyses, and the plasma neoclassical physics properties using the SFINCS code. These post-processing calculations provide a confirmation that the experimental goals of CSX can be achieved with the presented configuration.

Solar-pumped lasers, predominantly based on neodymium gain media, offer a promising route to renewable laser-energy conversion and space-based photonics; however, their performance has been constrained by thermal loading and limited power scalability. Here, we propose and numerically investigate a solar-pumped ytterbium thin-disk gain medium combined with a dome concentrator, which enables multipass solar pumping and enhanced absorption. The design yields comparably low lasing thresholds for neodymium- and ytterbium-doped media, while ytterbium provides superior power scalability, enabling up to threefold higher output power. We further identify ytterbium-doped medium combined with a spherical concentrator as a viable solar-pumped, radiation-balanced configuration, achieving self-cooled lasing at solar pump intensities of 28.5 kW cm-2 within the 1020-1033 nm window of the solar spectrum. We further demonstrate that dual-wavelength pumping overcomes the limitations imposed by low solar intensity and concentration constraints, enabling radiation-balanced lasing at orders-of-magnitude lower solar pump intensities. The proposed spherical-concentrator-based design enhances pump absorption while allowing efficient escape of anti-Stokes fluorescence. These results establish multi-pass, solar-pumped ytterbium lasers as a compact, scalable, and sustainable platform for high-performance solar-pumped lasers.