The dynamics of rolling tires significantly influence the low-frequency (0-500 Hz) structure-borne noise within vehicles. Accurately characterizing these dynamics under realistic operating conditions remains challenging. Current state-of-the-art methods, primarily relying on Laser Doppler Vibrometers (LDV), are complex to implement, time-intensive, and generally limited to smooth tires in laboratory environments due to issues with speckle formation on treaded surfaces. This study introduces an innovative strategy for Operational Modal Analysis (OMA) of a rolling tire using a single wireless Tire Cavity Accelerometer (TCA) together with two optical sensors. The methodology leverages the non-integer ratio between the tire and drum diameters in a test rig to create a virtual sensor array. By utilizing optical sensors to time-stamp the cleat impact (on the drum) precisely and the TCA position (on the tire), the vibration responses from multiple revolutions are clustered according to the TCA's circumferential position at the moment of impact. This effectively synthesizes responses from an array of virtual sensors distributed around the tire circumference using data from a single test run. The clustered signals are conditioned using order tracking to remove periodic components arising from contact patch deformation. Both Frequency Domain Decomposition (FDD) and Covariance-based Stochastic Subspace Identification (SSI-Cov) were employed for modal identification. The SSI-Cov method proved more robust, successfully identifying 11 circumferential modes up to 240 Hz. The proposed approach offers a significantly more efficient, cost-effective method for characterizing rolling tire dynamics, which is readily applicable to treaded tires and adaptable for on-road testing.

Pierre-Nicolas Bertholon's 18th-century electrovegetometer was conceived to harness "atmospheric electricity" for plant growth, yet its physical capabilities have never been quantified within the context of today's understanding of the Earth's atmospheric electric system. This study addresses the lack of quantitative assessment of such historical "electroculture" device and its plausible influence on the near-canopy electrical environment. It aims to reinterpret Bertholon's apparatus using contemporary atmospheric electrodynamics, asking under which realistic fair-weather and storm-like conditions a purely passive collector-distributor could generate fields and ion fluxes of physical significance. A two-dimensional, quasi-steady ohmic model has been developed in which the atmosphere is a resistive column carrying the global conduction current, the metal structure is a floating conductor supported by leaky wooden insulators and space-charge and corona are excluded so that all results describe pre-onset upper bounds. The simulations show that in fair weather the single upper point and the lower multi-point crown of the electrovegetometer enhance the background field by two to three orders of magnitude, yet only within millimetric-centimetric regions around the tips and with total currents limited to the pA-nA/m^2 range. Under storm-like forcing, peak fields at the crown reach 100-1000 kV/m, approaching or exceeding empirical corona-onset thresholds, while remaining largely insensitive to uncertainties in apex angle or collector geometry as long as an elevated mast is present. These results make Bertholon's reports of luminous "aigrettes" physically plausible, but suggest that any fair-weather agronomic impact was subtle and highly localized and that modern "electroculture" claims require careful, coupled electrostatic and biological studies beyond the pre-corona regime.

This review traces the historical trajectory of electricity in agriculture, from the earliest observations of electrical phenomena to the emergence of cold plasmas. Looking back to Antiquity and then to the Enlightenment, it underlines Abbé Bertholon's 18th-century efforts to channel atmospheric electricity to stimulate crops, using devices such as the electro-végétomètre. Although these early electroculture experiments relied on neither quantitative dosimetry nor rigorous methodology, they foreshadowed the idea of a controlled transfer of electrical energy to plants. Then the review examines the historical development of galvanism, electrochemistry, and the physics of gaseous discharges throughout the 19th and 20th centuries, which collectively laid the foundations for contemporary cold-plasma technologies. In the 21st century, plasma agriculture has emerged as an interdisciplinary approach integrating electrical, chemical, radiative, thermal, and fluid-mechanical effects. Applications include seed treatment (preconditioning, seed priming), stimulation of plant growth, soil and water treatment, and decontamination of agri-food products. The review thus reassesses Abbé Bertholon's contributions as those of a methodological precursor and shows how his intuition of a "vivifying electricity" resonates with modern cold-plasma science. Finally, it argues that plasma agriculture can transform an Enlightenment intuition into a reproducible experimental framework for sustainable agriculture and food safety.

For X-ray computed tomography (CT), a smaller detector pixel size generally leads to higher scanner spatial resolution, but inevitably increases system cost as well as data overhead in acquisition and processing. To achieve high-resolution (HR) CT imaging in a more resource-efficient manner, we propose a Foveated-Imaging Geometry CT (FIGCT) architecture, which integrates local HR data into an acquisition scheme dominated by low-resolution (LR) measurements. We further develop a Diffusion Probabilistic FIGCT Super-Resolution Reconstruction (DPFSR) framework to generate global HR CT images over the full field of view (FOV). The concept of FIGCT is first established, and its typical configurations are characterized according to the arrangement of HR data. Two key indices, namely the HR data fraction (HDF) and the LR-to-HR detector pixel size ratio (LHR), are introduced to describe the FIGCT geometry. The proposed DPFSR incorporates local HR information into intermediate clean-image estimates in both the projection and image domains during the reverse diffusion process. This additional step not only guides HR image generation from LR data, but also improves data consistency between the clean-image estimates and the originally measured data. Preliminary numerical simulation results on FIGCT show that the proposed architecture provides high-precision CT images within the region of interest (ROI) corresponding to the HR data, while the spatial resolution deteriorates rapidly outside the ROI. With DPFSR, global HR reconstruction is achieved on the AAPM Grand Challenge dataset and swine lung CT data, outperforming existing SR methods in terms of Learned Perceptual Image Patch Similarity (LPIPS), PSNR, and SSIM.

We report numerical simulations of a rotating rectangular annulus that isolates the Ekman-free bulk of the cylindrical baroclinic annulus, subjected to bi-directional temperature gradients imposed by a uniformly cooled inner wall and a localized forced heated plume at the outer bottom. The finite-volume OpenFOAM solver is employed across combinations of source Richardson number $Ri_0 = 99, 4, 1$ and Rossby number $Ro = 0.3, 0.1, 0.07$. A non-dimensional scaling of the governing equations identifies geostrophic-hydrostatic balance as the leading-order bulk state, a result confirmed a posteriori by the $x$ and $z-$momentum budgets. Baroclinic waves of mode $m=2$ at $Ro=0.3$ transition to $m=3$ as $Ro$ decreases, consistent with the contraction of the Eady deformation radius $L_ρ= NH/f$; Complex Empirical Orthogonal Function (CEOF) analysis characterizes the wave regime and detects a Hopf-bifurcated vacillating state at $Ri_0 = 99,~Ro = 0.1$. The plume morphology, classified through the Morton length scale and source flux-balance parameter, transitions from weak, laterally-swept structures at $Ri_0 = 99$ to sustained columnar plumes traversing the full baroclinic depth at $Ri_0 \leq 4$. The plume entrainment coefficient $Γ(z)$ shows opposite rotational sensitivities at low and high $Ri_0$, which we organize through a local plume Rossby number $Ro_p = w/(2Ωb)$. A mixing-length argument predicts a bulk turbulent heat flux $\overline{u'T'} \propto Ri_0^{-1/2}$, anticipating an order-of-magnitude enhancement from $Ri_0 = 99$ to $Ri_0 = 1$, in agreement with the simulations. A regime map in the $(Ri_0, Ro)$ plane reveals that, within the explored range, the plume-regime and wave-selection problems are approximately separable: $Ri_0$ sets the plume regime while $Ro$ selects the dominant baroclinic wave mode.

Bayesian inference provides a principled framework for uncertainty quantification in scientific machine learning. However, conventional Bayesian approaches usually require solving a new inference problem for each observation set, causing substantial computational costs that hinder real-time applications like online monitoring and digital twins. Furthermore, inferring over infinite-dimensional function spaces with varying observation sets poses major challenges for existing amortized inference methods. In this work, we propose Flow-ABI, a flow-based generative framework for amortized Bayesian inference in regression and inverse partial differential equation (PDE) problems. It consists of two components: (i) a functional prior model that learns expressive priors from historical data and physical knowledge through flow matching, and (ii) a set-conditioned functional posterior sampler mapping observation sets to functional posterior distributions. The learned posterior model naturally accommodates varying, permutation-invariant observation sets, and generalizes across different observation discretizations. Once trained, Flow-ABI enables near-real-time posterior sampling for previously unseen observations without retraining or iterative optimization. The proposed methodology can be seamlessly integrated with a wide class of scientific machine learning frameworks, including physics-informed neural networks and neural operators, for uncertainty-aware inverse PDE modeling. Experiments demonstrate that Flow-ABI accurately captures both Gaussian and non-Gaussian posterior distributions while achieving over two-order-of-magnitude speedups relative to the gold-standard Bayesian inference method, Hamiltonian Monte Carlo. These results show Flow-ABI is an effective, scalable, and computationally efficient framework for uncertainty quantification in scientific machine learning.

Continuous approximations of flow data are useful for downstream analysis, differentiation, and visualization, but purely data-driven reconstructions do not, in general, preserve the governing physics. This limitation becomes particularly important when input data are physically inconsistent, whether due to low-fidelity discretizations or unmodeled discrepancies. In such cases, reconstructed fields may exhibit inaccurate PDE residuals, violated balance laws, or unreliable derived quantities. To address this, we propose a physics-informed B-spline framework that embeds physical constraints directly into the reconstruction process. The method constructs compact, continuously differentiable representations of discrete fields using tensor-product B-splines and determines spline control points by solving an optimization problem balancing data fidelity with residuals of the governing PDEs, alongside initial and boundary conditions. Leveraging exact analytical derivatives of the B-spline basis enables efficient and accurate evaluation of physical residuals without storing full-resolution fields. We refer to this approach as physics-informed multivariate functional approximation (PI-MFA). Numerical studies on the 1D convection-diffusion, 2D coupled Burgers, and 2D incompressible Navier-Stokes equations show PI-MFA reduces PDE residuals and improves global balance-law consistency. Compared with standard and regularized MFA, PI-MFA produces more physically faithful reconstructions and, for physically inconsistent data, lower approximation errors, while offering computational advantages over tested physics-informed neural networks. Overall, PI-MFA preserves the compactness, local support, and exact differentiability of classical spline spaces while producing reliable continuous flow fields for scientific analysis and visualization.

The neoclassical toroidal viscosity (NTV) torque is a critical driver of toroidal rotation in tokamaks, profoundly influencing plasma stability and performance. Consequently, incorporating NTV effects is essential for modern integrated modeling frameworks that aim to self-consistently unify multiple physical processes. However, the high computational cost of NTV modeling precludes its self-consistent integration within such frameworks. This bottleneck arises because NTV calculation requires solving its governing equation--the drift kinetic equation (DKE)--in high-dimensional phase space. To address this issue, this study develops DKEKAN, a single-parameterized Kolmogorov-Arnold Network (SKAN) surrogate for solving DKE, to realize fast NTV modeling in tokamaks. The research process consists of the following steps: Firstly, a large dataset mapping DKE equation parameters to solutions is generated based on first-principle simulations under plasma parameters of the Experimental Advanced Superconducting Tokamak (EAST); Secondly, a surrogate model for solving DKE is developed based on the SKAN framework, which also incorporates a modular expert network design; Finally, the DKEKAN surrogate model is integrated with the NTV modeling framework to realize fast NTV calculation. With its physics-grouped expert layer and SKAN backbone, DKEKAN outperforms the tested MLP, KAN, and neural-operator baselines in overall prediction accuracy, while reducing the standalone DKE-solving time from 35.85s to 3.74s, corresponding to a speedup of approximately 9.6x, and reducing the total coupled NTVTOK runtime from 38.24s to 5.58s, corresponding to an overall speedup of approximately 6.9x. This work effectively overcomes the computational bottleneck in NTV simulations, thus supporting further integrated modeling that incorporates NTV effects.

Attosecond pulse trains interacting with a resonantly dressed atom generate a pronounced intra-sideband phase structure that remains hidden in conventional spectrally integrated RABBITT measurements. Using \textit{ab initio} time-dependent Schrödinger equation calculations for lithium near the resonant $2s\to2p$ transition, we show that the phase extracted within a single sideband can vary by nearly $π$ across its spectral width. The resulting intra-sideband phase dispersion exhibits a characteristic dependence on the IR detuning, pulse duration, intensity, and sideband order. Most strikingly, exact resonant Rabi flopping flattens the intra-sideband phase dispersion, whereas a small detuning generates a pronounced phase modulation despite weaker population transfer. This counterintuitive behavior demonstrates that rainbow RABBITT probes the dynamical phase accumulated by a Rabi-dressed wave packet rather than the instantaneous populations of the participating states. A simple analytical model captures the principal features of the numerical calculations and provides physical insight into the emergence of the intra-sideband phase structure. These results establish intra-sideband phase dispersion as a new interferometric observable for mapping coherent Rabi dynamics.

In this work, we investigate the response of Ni/$β$-Ga$_2$O$_3$ Schottky barrier diodes fabricated on c-plane sapphire to fast-neutron irradiation up to a fluence of $1\times10^{15}$ n$\cdot$cm$^{-2}$. The LPCVD-grown heteroepitaxial structure consists of an unintentionally doped buffer, an n$^{+}$ contact layer, and an n-type drift layer, with mesa isolation realized by plasma-free Ga-assisted LPCVD etching. Prior to irradiation, the devices exhibit a turn-on voltage of 1.20 V, specific on-resistance of 8.43 m$Ω\cdot$cm$^2$, ideality factor of 1.32, and Schottky barrier height of 1.29 eV. Following irradiation, the devices remain operational, although the forward current decreases, the turn-on voltage increases to 2.40 V, and the barrier height increases to 1.34 eV. Capacitance-voltage measurements reveal a $\sim$50% reduction in net donor concentration, corresponding to a carrier-removal rate of $\sim$105 cm$^{-1}$. Temperature-dependent measurements from 25 to 250 $^\circ$C confirm that thermionic emission remains the dominant transport mechanism and show significant suppression of reverse leakage current after irradiation. The breakdown voltage increases from 101 to 135 V, consistent with neutron-induced donor compensation. TCAD simulations show a more uniform electric-field distribution and reduced field crowding at the Schottky edge after irradiation. These results provide insight into neutron-induced donor compensation in heteroepitaxial $β$-Ga$_2$O$_3$ and demonstrate the ability of LPCVD-grown $β$-Ga$_2$O$_3$ Schottky diodes on sapphire to maintain stable operation under high-fluence neutron environments relevant to space and nuclear electronics.

Microscale locomotion in Newtonian fluids is constrained by kinematic reversibility. Here we show that viscoelasticity provides a distinct route: an achiral fore-aft asymmetric body rotating in a viscoelastic fluid generates net translation through normal-stress-driven secondary flows. Direct numerical simulations combined with scaling analysis reveal the universal scaling law, $V\sim {\rm Wi}\cdot S$, where $\rm Wi$ is the Weissenberg number and $S$ is the skewness of the axial volume distribution. This result identifies a minimal geometric principle for rotation-induced propulsion in viscoelastic fluids, and suggests a route for active microrheology via propulsion-speed measurements.

Spatial multiplexing is one of the natural strengths of optics, yet in optical neural networks, it is often used mainly as parallel throughput. Here, we show that spatial multiplexing in an optical neural network can be used not only to process multiple inputs in parallel, but also to define a trainable representational coordinate of the model. In three implemented scenarios, parallel-input processing, class-code readout and channel-mixed feature interaction, spatial channels act as independent learners, structured code dimensions, and interacting feature groups. The programmable free-space optical processor is trained through an online physical-forward/surrogate-backward scheme, where measured optical outputs define the forward pass while a differentiable surrogate estimates gradients and is continually fine-tuned during training from newly acquired optical data. We demonstrate these channel roles in image classification and regression tasks using multi-layer architectures with more than one million trainable optical phase parameters. We further implement a hybrid optical-electronic vision-language model, in which the optical neural network provides visual tokens to a digital transformer decoder for controlled image-captioning tasks. These results establish spatially multiplexed optical channels as a programmable feature and readout space for hybrid optical vision models.

Photodiodes based on trap-assisted current injection can exhibit internal photomultiplication with apparent quantum efficiencies far exceeding unity, raising the question of whether such gain fundamentally enhances detector sensitivity. We employ a minimal analytical framework based on a single gain-active trapped state coupling photogenerated carriers to contact injection. The gain is intrinsically self-limiting: the injection process that amplifies the current simultaneously accelerates relaxation of the gain-enabling state, producing an inherently nonlinear, operating-point-dependent response. The form of this nonlinearity is not universal -- once the trap level is generalized to an energetic distribution and recombination is allowed to be bimolecular, the same mechanism yields superlinear, linear, or strongly sublinear responses. A single chord gain is therefore not a meaningful device descriptor, and chord-gain comparisons across the literature conflate devices in different regimes. Treating trap occupancy and injection as coupled stochastic processes, we show that internal gain introduces a strictly non-negative fluctuation penalty from the dissipative dynamics that sustain the gain state. A local, small-signal detectivity exhibits a finite optimum yet cannot exceed the intrinsic thermodynamic limit of the underlying unity-gain photodiode. Gain is thus equivalent to driven stochastic amplification: it can suppress downstream readout noise, but cannot reduce the fundamental noise floor set by the primary photodetection process.

We report on the optimization of high voltage vacuum feedthroughs meant to minimize the electric field on the vacuum side of the device. We find that commercial feedthroughs generally have center conductor diameters which are too small, resulting in unnecessarily large fields. We study the problem with analytical calculations and finite element analysis, and present a simple, optimized retrofit for the commercial feedthroughs studied here, without compromising their outgassing properties. This work is important for applications whereby the ``vacuum side'' of the feedthrough is, in fact, filled with a dielectric which may not have the voltage rigidity of vacuum.

Characterizing Europa's subsurface ocean is a key objective of the Europa Clipper and JUICE missions in the search for life beyond Earth. Although the ocean's induced magnetic field provides key constraints on habitability, interpretation is complicated by perturbations arising from Jupiter's plasma interaction with Europa. Physics-based models (e.g. magnetohydrodynamic, MHD) required to characterize these effects are physically comprehensive, but have a prohibitive computational cost. To address this, we introduce Learning Europa's Atmosphere and Plasma (LEAP), a transformer-based surrogate trained on outputs from a state-of-the-art multi-fluid MHD code to predict magnetic field perturbations along spacecraft trajectories. LEAP evaluates in milliseconds on a laptop, whereas MHD takes 12 hrs on a high-performance computer (~40,000x speed-up). The model has test set errors of -/+ 2.6 nT, and for the Galileo E4 and E14 flybys of Europa it matches the parent MHD model in accuracy. Its enhanced speed enables large-scale parameter surveys and probabilistic estimations of plasma conditions, establishing a new framework for accelerated plasma interaction modeling. LEAP can also inform future MHD simulations while learning from them. Beyond Europa, this framework could be expanded to planning future missions or to other high-priority bodies, including Uranus and Neptune.

Magnetic fields have shown potential to affect flame characteristics; however, the mechanisms of interaction are not fully understood. This paper investigates the effect of magnetic fields on premixed hydrogen-air flames that are prone to intrinsic instabilities, with a focus on the role of magnetic forces on the flame behaviour. The study is conducted using direct numerical simulations. Two flame conditions, both with an equivalence ratio of 0.5, are studied, one with the reactants at atmospheric conditions and the other at high pressure and high temperature. Different configurations of the magnetic field are investigated, each characterised by a different gradient of the square of the magnitude of the magnetic field, oriented in the direction opposite to the velocity of the incoming reactants. Results show that the investigated configurations of the magnetic field can reduce the flame consumption speed, an effect that is substantial in the lower pressure case, while it becomes negligible at high pressure. The effect of the magnetic forces increases with increasing gradient of the magnetic field and is mainly due to the reduction of the flame area. Results also show that the effects of magnetic fields on the reactivity of the flame and on the small cell structures developed along the flame front are negligible. Analysis of the force contributions demonstrates that the change in the flame area is caused by the rotational component of the magnetic forces, which alter the vorticity of the flow such that the finger-like structures formed by hydrodynamic instabilities tend to close. These forces are significant at low pressure, while they become negligible compared to the pressure gradient at high pressure. Ultimately, the results of this work indicate that magnetic forces have the potential to change the flame behaviour, a mechanism that could be used for active control of flames.

We present a Rydberg-Atom electric field sensor optimized to detect signals at sub-100 MHz carrier frequencies. The sensing setup employs a sapphire vapor cell that allows for detection of signals below 100~MHz -- typical vapor cells made of glass or quartz demonstrate strong screening of radio frequency (RF) signals in this frequency regime. Applied signals are detected by observing AC Stark shifts in the atomic vapor energy levels. As a test case for the commercial utility of this receiver, we perform our tests at several carrier frequencies in the Industrial, Scientific, and Medical (ISM) band. At each carrier frequency, we report sensitivity, minimum detectable field, and detectable electric-field dynamic range. We also present a routine for optimizing off-resonant signal detection by tuning experimental parameters such as Rydberg coupler laser detuning and RF local oscillator strength. This Python-based optimization routine, which can be used at any off-resonant carrier frequency, is shared on Github for others to use in their own investigations.

Recently developed 15 um, 20 um, 25 um and 30 um cell size Hamamatsu Silicon Photomultipliers (SiPMs) were irradiated with reactor neutrons at JSI (Ljubljana) up to 2*10^14 n/cm^2 (1 MeV equivalent). The parameters of new and irradiated SiPMs were studied using pulsed light illumination. The effects of the neutron radiation on breakdown voltage, signal amplitude, dark current and noise for these devices are shown and discussed.

The light harvesting 2 (LH2) complex of purple bacteria has excellent energy conversion efficiency. Clarifying the design principle behind such efficiency at the atomistic level is crucial for understanding its structure-function relationship, and can be utilized for the design of artificial light harvesting systems. To this end, we conducted comprehensive computational investigation of the dynamical and statistical nature of electronic excited states of pigment molecules in a natural LH2 complex with 9-fold symmetry and its two non-natural {\it in silico} analogues with 6- and 12-fold symmetries. To ensure reliable and efficient all-atomistic molecular dynamics simulations, we combined a well established interpolation approach for the construction of the potential energy surface with a neural network machine learning approach. Outcomes of these calculations clarify that non-natural forms of LH2-type complexes have significantly larger quasistatic disorder than those for the natural one. In addition, non-natural systems have more disruptions of the hydrogen bonding, underscoring its crucial role for reducing the disorder. On the other hand, local environmental dynamics are relatively insensitive to the structural changes although there is moderate enhancement in the anharmonic or interatomic components for the synthetic ones. These findings based on all-atomistic simulations provide direct computational evidence that the structure and sizes of natural LH2 complexes are designed to minimize the energetic disorder. We analyze quantitative implications of these for the energy transferring capability of the LH2 complex.

We report a mass rule of Koide type with inverse shape, \[m_i=M^{(d)} (w_0+w_i)^{-2}.\] It applies to the down-quark sector with numerical precision comparable to that of the direct charged-lepton sum rule $m_i=M^{(l)} (z_0+z_i)^{2}$. For central mass values, Koide ratio reaches exactly $2/3$ near 280 TeV under Standard Model renormalisation-group running. We also review other rules of the direct kind involving quarks.

While electrochemical oxidation is a promising technology for water treatment, a fundamental understanding of the specific radical mechanisms involved in pharmaceutical degradation has remained limited. This study addresses this gap by employing Electron Paramagnetic Resonance (EPR) spectroscopy to directly quantify the radical species generated during the degradation of paracetamol using a novel gas diffusion electrode (GDE) modified with NaNbO3 nanocubes and CeO2 nanorods. This approach provides a critical advancement beyond prior literature by moving from indirect inference to direct, quantitative analysis of reactive species. Results demonstrated that a boron-doped diamond (BDD) anode (65% OH radical, 35% aryl radicals) drastically outperformed a Platinum (Pt) anode (74% OH radical, 26% aryl radicals), achieving complete degradation in 15 minutes versus 45 minutes and 81.6% versus 67.8% mineralization. Consequently, this work provides a foundational mechanistic framework that fundamentally advances the field, offering not just a more effective material system (BDD/NaNbO3@CeO2-GDE) but also a validated methodology for rationally designing and optimizing electrochemical water treatment processes based on quantifiable radical pathways.

Modern atomic spectroscopy has reached a level of precision at which nuclear-structure effects can no longer be neglected and must be quantified reliably. In particular, hyperfine splittings depend on the Zemach radius, which encodes the convolution of the nuclear charge and magnetization distributions. The third electric Zemach moment provides a related finite-size measure and enters the elastic two-photon-exchange contribution to the Lamb shift in muonic atoms. Here, we compute Zemach radii and other electromagnetic moments for light nuclei using quantum Monte Carlo techniques within modern \textit{ab initio} nuclear theory. Using Norfolk two- and three-body interactions derived within chiral effective field theory, we assess the model dependence and study the role of two-body currents. For $^6$Li, we obtain a Zemach radius larger than that extracted from atomic measurements, consistent with recent calculations, confirming that the discrepancy is not an artifact of the nuclear model. For $^9$Be, our results agree with experiment; the discrepancy of previous phenomenological evaluations is traced to a model-dependent input for the magnetic radius.

We propose simultaneous $αβ$ decay as a novel mode of nuclear instability that involves the strong and weak interactions in a single quantum transition. We develop a theoretical framework to predict its branching ratios and $α$-energy spectra, establishing exclusive and inclusive criteria based on whether the individual $α$ and $β$ channels are closed or open. A global survey of the nuclear chart identifies five exclusive $αβ^-$ candidates, all predicted to be experimentally inaccessible, and ranks the leading inclusive candidates for both the $αβ^-$ and $αβ^+$ modes. Remarkably, six of the top $αβ^+$ candidates coincide with known $β$-delayed-$α$ precursors. The observed $α$ spectra are naturally accounted for by simultaneous $αβ^+$ emission, suggesting direct decay as the dominant underlying mechanism. Our findings establish simultaneous $αβ$ decay as a distinct radioactive process and a sensitive probe of the interplay between the strong and weak interactions.

We investigate the global spin polarization of $Λ$ hyperons and the multi-strange hyperons $Ξ^{-}$ and $Ω^{-}$ in Au+Au collisions across the RHIC Beam Energy Scan II (BES-II) energy range, $\sqrt{s_{NN}}=7.7$--$27$ GeV. The polarization is computed using the modified Cooper--Frye formula, which includes contributions from thermal vorticity, the thermal shear tensor, and the gradient of the baryon chemical potential, combined with the (3+1)-dimensional viscous hydrodynamic framework CLVisc with SMASH initial conditions. We present the global polarization as a function of collision energy, centrality, transverse momentum, and rapidity. We find that the global polarization of $Ω^{-}$ is systematically larger than those of $Λ$ and $Ξ^{-}$ because of its larger spin quantum number, but it remains below the central value of the recent STAR measurement. This discrepancy may suggest that additional mechanisms, such as spin correlations among strange quarks inside the $Ω^{-}$, could contribute to the observed $Ω^{-}$ polarization. We also find that the global-polarization splitting between hyperons and anti-hyperons increases toward lower collision energies and is dominated by the chemical-potential-gradient contribution.

The Th-229 nuclear isomeric state has the lowest energy of all known nuclear excited states, placing it within the reach of current table-top laser technology. This extraordinary property has made this nuclear isomer an attractive candidate for a nuclear optical clock of incredibly high precision and accuracy, both as isolated trapped Th-229 ions and embedded into solid-state platforms. Activity around Th-229 has surged in recent years, driven by breakthroughs in its direct laser excitation. The underlying nuclear physics that gives rise to this unique isomer will be elucidated, as well as the nearly half-century of efforts that led to its direct excitation. The design and systematics of a Th-229 nuclear clock will be discussed, both in ion traps and in the solid-state. These systematics, such as frequency shifts and quenching channels, can be leveraged both to probe the local chemical environment, and as a control knob during clock operation. Finally, the nuclear clock's high sensitivity to the variations of fundamental constants will be discussed.

Neutron noise methods are used to determine kinetic parameters such as the prompt neutron decay constant, but traditional pulse-counting suffers from dead-time and pile-up at high detection rates. Recent theory shows that analysing the continuous detector current can avoid these limitations if pulse-shape effects are properly treated. This work presents a feasibility study of continuous-signal neutron noise analysis based on simulations and experiments performed at two research reactors. The stochastic model of the detector current is applied to derive Rossi- and Feynman-type formulations, and pulse-shape distortions are mitigated using detector pairs or by deconvolving the average pulse-shape through inverse Fourier and Wiener filtering. Simulations demonstrate accurate $α$-parameter estimation at count rates where pulse-counting becomes unusable, and enable evaluation of significantly higher $α$ values. Measurements at KUCA and BME TR confirm that continuous and deconvolved signals provide unbiased results despite dead-time and electronic artifacts, establishing the method as a practical alternative for high-rate reactor noise diagnostics.

The absolute number of negative muons contained in a beam is essential for many experiments at accelerator facilities, but determining it in pulsed beams has been difficult, particularly at high intensities. The method utilizing the yield of the $β$ delayed $γ$ rays from the residual nuclei after the muon nuclear capture reaction has recently been developed to determine the muon number in the pulsed muon beam. In particular, the in-beam activation method employs isotopes with short lifetimes, enabling the beam intensity to be measured over a short period with irradiating muon beams. However, only a limited number of isotopes have reliable measurements of production branching ratios (BRs), which are required to determine the absolute muon number in the pulsed beam. To search for new candidate isotopes that are suitable for in-beam activation method, the production branching ratio after the muon nuclear capture reaction was measured for natural abundance Cu, Zn, and Ag. Considering the strength of the BR, the muon capture probability, the practical detection efficiency of the detector, and rarity of the target material in the surrounding structures, the reaction $^\mathrm{nat}$Ag ($μ^-, ν_μx$) $^{107m}$Pd is found to be a useful reference for the muon number calibration.

The lifetimes of the low-lying negative-parity $1^-$ state at 1414~keV and $2^-$ state at 1398~keV in $^{154}$Gd have been measured using the $γ$--$γ$ fast-timing technique with the VENTURE array at VECC, Kolkata. The states were populated through the $β$ decay of $^{154}$Tb, produced in proton-induced reactions at the K130 cyclotron. From the measured lifetimes, absolute $B(E1)$ transition strengths were deduced. The extracted $B(E1)$ values are compared with those of neighboring Gd isotopes and with Gogny-HFB-based $sdf$-IBM calculations. The results show that the measured $E1$ strengths from these states are strongly hindered compared with the corresponding $ΔK=0$ transitions, providing evidence for weak $ΔK=1$ dipole strength in $^{154}$Gd.

Adaptive systems can exhibit similar levels of performance while relying on fundamentally different internal modes of coordination. Standard metrics such as average cooperation or payoff indicate whether a system succeeds, but do not reveal how coordination is organized across interacting components or which adaptive variables should be targeted when performance fails. Here we propose complexity synchronization (CS), the synchronization of evolving temporal complexity across coupled variables, as a diagnostic and intervention guiding principle for adaptive systems. We test this idea in an adaptive multi agent system composed of Selfish Algorithm agents interacting in a reduced Predator Prey model with a Prisoners Dilemma like payoff structure. Temporal complexity is quantified using sliding window modified diffusion entropy analysis (MDEA) and detrended fluctuation analysis (DFA). CS is defined as the correlation between the resulting time dependent scaling exponents. In the high-interaction regime, MDEA-based CS increases with cooperative performance, whereas DFA based CS captures a distinct persistence dominated coordination mode. Our results show that CS can reveal functionally relevant subsystems and provide a principled basis for targeted repair. More broadly, CS offers a general diagnostic and engineering framework for understanding and controlling coordination in biological, social, human machine, and other adaptive systems.

We investigate the long-time asymptotics of a full soliton gas for the Camassa--Holm equation. The analysis starts from a pure-soliton Riemann--Hilbert (RH) problem with \(2N\) poles and two distinct types of residue conditions. We prove that, as \(N\to\infty\), this discrete RH problem converges to a limiting soliton gas RH problem whose jump matrix contains two nonzero reflection coefficients. In this sense, the limiting problem gives a full soliton gas model for the Camassa--Holm equation, in contrast to the previously studied half soliton gas models, whose jump matrices involve only one nonzero reflection coefficient. The limiting RH problem is analyzed by the Deift--Zhou nonlinear steepest descent method. The presence of two nonzero reflection coefficients requires two different types of triangular factorizations of the jump matrix and leads to a more delicate \(g\)-function mechanism. The main difficulty lies in the construction of suitable \(g\)-functions adapted to the Camassa--Holm phase, together with the precise control of their behavior near the distinguished point \(k=i/2\) and at infinity. Depending on the location of the spectral endpoints \(η_1\) and \(η_2\), different \(g\)-function mechanisms arise. In this paper, we focus on Case I and derive the long-time asymptotic formulas in three elliptic-wave regions of the self-similar plane. In each region, the leading term is given by a finite-gap elliptic function, while in the central region the first correction is of order \(\mathcal O(t^{-1/2})\) and involves parabolic cylinder functions.