A transmitting intelligent surfaces (TISs) aided satellite indoor navigation system is investigated. By leveraging the unique features of TIS, we address the limitations of conventional global navigation satellite systems (GNSS) in providing reliable positioning services within indoor environments. To facilitate the extension of GNSS indoor signals, we establish an extended line-of-sight link using TIS which has the capability to change signal direction. A three-stage TIS-aided satellite indoor positioning algorithm (TSIPA), which utilizes the positions of TIS arrays and the angle of arrival, is proposed to locate indoor users. To evaluate the distribution of TIS arrays, we propose TIS position dilution of precision (TPDoP) to evaluate centroid deviation and utilize the root mean square error (RMSE) to represent compactness.

Multimodal signals on sensor networks are commonly modeled under the twofold graph assumption (TGA), which represents spatial structure and inter-modality relations as two separate graphs. Existing TGA-based signal restoration methods, however, either assume the graphs are known or restrict edge weights to be non-negative, preventing them from capturing negative inter-modal correlations. We address both limitations by formulating joint signal restoration and twofold graph learning as MAP estimation under a matrix normal prior, where the spatial and modality graph Laplacians appear directly as precision matrices. The resulting non-convex objective is solved by alternating minimization: The signal is updated via conjugate gradient applied to the arising Sylvester-type linear system; the graphs are updated via primal-dual hybrid gradient (PDHG). We further propose a method to estimate the signed structure of the modality graph from the dominant eigenspace of a complementary kernel matrix, which is then used in PDHG to update edge magnitudes. These iterative solvers are then unrolled into a feedforward network, with regularization weights and step sizes as layer-wise trainable parameters. Experiments on synthetic multimodal graph signals and a real Japan meteorological dataset confirm that the proposed method outperforms existing baselines across a range of noise levels and missing-data patterns.

Biophotons are ultra-weak photon emissions in the visible spectrum produced by living organisms. While extensively studied in plants, germinating seeds, and cell cultures, no systematic multi-method complexity analysis of human ultraweak photon emission (UPE) under physiological modulation has been reported. We address this gap by applying a comprehensive analytical framework to UPE measurements from the right palm of a human subject. Three independent sessions were conducted on different days, each comprising four consecutive 15-minute phases: Dark reference, pre-meditation resting state (Pre), structured meditation based on the Sama Vritti box-breathing protocol, and post-meditation recovery (Post). Photon count series are analysed with four complementary methods: distributional statistics (Fano factor, skewness, tail Expected Shortfall); multiscale Fano factor and Allan deviation; stripe-filtered Diffusion Entropy Analysis (DEA); and Renyi entropy with a Time Reversal test. The methods show complementary sensitivities, converging on a coherent picture: a systematic reduction of emission intermittency during meditation, consistently detected across all three sessions. Stripe-filtered DEA places the emission in the non-ergodic renewal regime with a Pre-to-Meditation decrease of the scaling exponent. Renyi analysis reveals two effects: reduced marginal amplitude burstiness (Tdir) and increased sequential pattern structure (Tseq), interpreted as entrainment to the Sama Vritti rhythm. These findings are consistent with cardiac complexity transitions during meditation reported by Tuladhar et al. and with EEG reorganization during Sama Vritti breathing by Zaccaro et al., suggesting a coordinated multi-channel physiological response. The results establish a proof-of-concept framework for complexity analysis of human UPE under physiological modulation.

Near-term and early fault-tolerant quantum computing architectures are expected to exhibit highly non-uniform error rates. In particular, local operations within a chip can be substantially more reliable than operations connecting different chips or dilution refrigerators. Such inter-module operations can therefore become a dominant bottleneck, even when quantum error correction is applied. Entanglement distillation provides a natural way to trade additional operations and qubits for higher-fidelity entanglement. Standard distillation protocols, however, are usually formulated in an LOCC resource model, in which several noisy Bell pairs are generated initially and all subsequent processing consists only of local operations and classical communication. Here, we consider a generalized model tailored to modular quantum computing hardware, in which the two modules have access to high-fidelity local operations and to repeated uses of the same noisy inter-module entangling operation during the protocol. We develop practical small-scale entanglement distillation protocols designed to minimize both space and time overhead. Remarkably, our main protocol requires only two qubits per module, yet achieves quadratic error suppression of inter-module errors, assuming local operations are much cleaner. Compared with existing small-scale protocols, our space-optimal protocol provides more space- and time-efficient quadratic error suppression and achieves the best performance in our simulations and experiments on noisy links of current superconducting quantum processors. These results suggest that inter-module-gate-assisted entanglement distillation can be a practical primitive for overcoming noisy links in modular quantum computing architectures.

Protesting mildly against the notion of an exactly correct parametric model the view is adopted that the logistic regression equation is merely an approximation to the underlying, true function. The behaviour of likelihood based estimators is investigated in such a general framework. The maximum likelihood estimator is shown to be consistent for a certain least false parameter value minimising a weighted average of quantities that measure the distance from the true to the parametric model. Asymptotic normality is also demonstrated. Finally a number of additional remarks are offered, some pointing to natural generalisations and some to new questions for research, like weighted and local likelihood estimation methods.

Ultrasound Localization Microscopy (ULM) has presented great potential in functional imaging, benefiting from its ability to reconstruct deep microvasculature. However, the hemodynamic reconstruction is compromised by sparsity in the ULM data, as a limited number of MB tracks cannot sample the complete speed profile in one vessel. Here, we propose to reconstruct hemodynamics using sparse ULM velocity maps by solving a laminar flow model through stochastic variational inference. In addition to vascular geometry and flow velocity maps, the proposed method generates two new ULM maps - a pressure gradient map and a map describing uncertainty of the estimation. By investigating the effect of sparsity in ULM maps on the quantification and visualization of hemodynamics, we demonstrate the effectiveness of the proposed method in dealing with sparse ULM maps via simulations and 3D rat brain imaging. Accurately reconstructing a broad range of hemodynamic parameters and associate uncertanties using sparse ULM data may help detect subtle and dynamic brain activity.

In this paper we propose dynamic output-feedback controller synthesis methods for discrete-time linear time-invariant systems. The synthesis goal is either to achieve dissipativity with respect to a given quadratic supply rate, or to achieve given $H_2$ performance level. It is assumed that the autoregressive model of system dynamics is unknown, expect for the noisy disturbance term which is not part of the performance channel. Instead, we have a recorded trajectory of inputs and outputs which can be corrupted by an unknown but bounded disturbance. Methods are formulated in terms of linear matrix inequalities parametrized by a scalar variable, while in noiseless case they reduce to linear matrix inequalities. Within the considered setting, synthesis procedures are non-conservative.

Physical layer security in reconfigurable intelligent surface (RIS)-assisted wireless systems can be improved through coordinated control of signal transmission and RIS configuration. In this work, the base station simultaneously transmits the communication signal (CS) and artificial noise (AN) in the presence of a potential eavesdropper. The RIS is partitioned into two groups of reflecting elements, where a portion enhances the desired CS toward the legitimate receiver, while the remaining elements contribute to AN transmission. Two key parameters govern the system design: a transmit power allocation factor between CS and AN, and an RIS element allocation ratio controlling the partitioning of the reflecting elements. An iterative binary phase optimization strategy is employed to enhance the received signal power at Bob while degrading Eve's reception. Simulation and experimental results demonstrate that proper joint design significantly improves the achievable secrecy capacity.

The group isomorphism problem in computational complexity asks whether two finite groups given by their Cayley tables are isomorphic or not. Although polynomial-time isomorphism tests exist for many specific types of groups, no general polynomial-time algorithm is known, classes of solvable and nilpotent groups being the main obstacles. In 2012 Babai and Qiao gave a polynomial-time isomorphism test for the class of solvable groups admitting normal series with abelian Sylow factors. We generalize their result and give a polynomial-time isomorphism test for A-groups, i.e. groups with abelian Sylow subgroups. The algorithm heavily relies both on the computational methods developed by Babai and Qiao, and structural properties of A-groups.

Many turbulent flows encountered in nature -- seas, oceans and rivers -- are bounded by a deformable free surface. A question that remained to be fully explored is to what extent the underlying turbulent flow field can be revealed solely by observing the surface deformations. In this study, we attempt to correlate free-surface topological deformations with the underlying turbulent flow field. We report an experimental investigation of the free surface in the wake of a surface-piercing cylinder and turbulence created by an active grid in an open-channel flow. We are able to study instantaneous events of surface indentations and their related sub-surface coherent structures, as well as statistical properties of velocity and surface motion. We observe weak cross-correlation between the vorticity field and the surface when considering the global surface elevation field. Slightly stronger correlations emerge when conditioning the surface on specific regions, even in the case of three-dimensional homogeneous isotropic turbulence.

Time-dependent partial differential equations (PDEs) often develop sharp fronts, localized peaks, and other moving structures that occupy only a small portion of the space--time domain but dominate the approximation error. This makes fixed or uniformly sampled collocation strategies inefficient for physics-informed neural networks (PINNs), especially in high dimensions and over long-time prediction intervals. We propose the predictive moving sample method (PMSM), which builds on the moving sample method (MSM) in \cite{xu2026moving} by replacing its full time domain iterative training with a progressive time-stepping strategy and simplifying the velocity-field loss to further reduce the per-step cost. To improve practicality for long-time prediction, we further introduce the windowed-reset predictive moving sample method (WR-PMSM), which restricts extension training to an active time window and periodically resets the reference state, thereby reducing the growth of optimization cost while preserving global consistency through a final refinement stage. Across four representative benchmarks, PMSM consistently outperforms both standard PINNs and the original MSM under matched collocation budgets. These results suggest that transporting samples according to residual dynamics provides an effective and practical route to neural network solvers for time-dependent PDEs.

We study the finite-temperature Casimir effect for a massless scalar field confined between two parallel plates in a Schwarzschild-like wormhole spacetime. Imposing Dirichlet boundary conditions, we compute the renormalized Casimir free energy in the comoving frame. We find that the thermal correction to the renormalized Casimir free energy decreases gradually with the temperature and becomes independent of the background geometry in this frame. Thermodynamic quantities derived from the Casimir free energy, namely, the renormalized Casimir entropy, internal energy, and heat capacity at constant volume, exhibit distinct temperature dependence. At low temperatures, all thermodynamic quantities recover the expected behavior, consistent with the fundamental laws of thermodynamics. These results provide a compact framework for analyzing quantum vacuum forces in gravitational backgrounds.

Stellar granulation arises from near-surface convection and is imprinted in stellar photometric time series, yet links between granulation observables and fundamental stellar properties remain underexploited. We aim to establish a statistically robust framework for inferring stellar radii directly from granulation signals in long-duration space-based photometry, aided by atmospheric parameters. We construct a Bayesian hierarchical model to connect stellar radius and granulation, relating radius through regression to the total granulation amplitude, primary characteristic frequency of the granulation, stellar effective temperature, and surface metallicity. The derivation is performed separately for three granulation models, propagating the marginal posteriors of the granulation parameters to account for intrinsic dispersion of the derived relations. Each background model yields a unique radius posterior, subsequently combined using Bayesian evidences as weights, producing posteriors that best represent the given star. The granulation-radius relations were derived from a heterogeneous sample of 363 stars, combining seismic and interferometric targets from multiple sources. Application to an independent sample of 367 stars recovers the reference radii within $1σ$ in ${\approx}73\%$ of cases. The distribution of residuals is consistent with a well-calibrated and unbiased inference. Across applications, the granulation-inferred radii achieve a precision of ${\approx}10\%$. The agreement with seismic and interferometric benchmarks demonstrates that granulation carries predictive information on stellar radii at a level comparable to several established techniques. Using granulation as a structural diagnostic enables the inference of stellar radii from granulation signals across diverse stellar populations; directly applicable to data from Kepler, TESS, and the upcoming ESA PLATO mission.

Ownership concentration is not a scalar. For a normalized investor-stock matrix $A$, it has three irreducible layers: concentration across investors, concentration across stocks, and dependence in the joint assignment of investors to stocks. This paper develops a unified quadratic framework for those layers and shows that the same residual operator that measures static overlap also governs linearized market transmission. Raw micro concentration $M(A) = \sum_{i,j} A_{ij}^2$ admits exact row and column decompositions, support bounds, and fixed-marginal extremal characterizations on the transportation polytope. Benchmark-adjusted dependence $\mathcal{X}(A) = \sum_{i,j} (A_{ij} - p_i s_j)^2 / (p_i s_j)$ admits two exact decompositions: it is a size-weighted average of investor-level deviations from the market portfolio and, symmetrically, of stock-level deviations from the investor base. The paper also proves a multiscale aggregation law: under any partition of investors, total dependence splits exactly into between-group dependence and within-group heterogeneity. Spectrally, $\mathcal{X}(A)$ equals the sum of squared nontrivial singular values of the whitened matrix $D_p^{-1/2} A D_s^{-1/2}$. The residual operator $L$ then yields two dynamic consequences: idiosyncratic fire-sale vulnerability is bounded by the dominant overlap mode $ρ(A)$, while aggregate benchmark-relative alpha variance has worst-case capacity $ρ(A)^2$ and isotropic average-case capacity $\mathcal{X}(A)$. The fixed-marginal geometry also motivates a feasible-range sparsity score that benchmarks observed micro concentration against the sharp minimum and maximum implied by the marginals. The resulting framework separates scale concentration, feasible sparsity, overlap, and linear transmission in a way that is mathematically transparent and empirically usable for work on crowding, fragility, and systemic risk.

We introduce the Deontic Action Model Logic (DAML), a dynamic modal framework for reasoning about obligations over actions in multi-agent systems. DAML extends the epistemic Action Model Logic by incorporating deontic evaluation mechanisms that assess agents' actions in terms of both the desirability and the likelihood of their outcomes. Obligations arise for those actions that maximize expected deontic value among an agent's available alternatives at a given decision point, yielding a formal account for reasoning about conditional and context-sensitive obligations in settings involving strategic interaction and incomplete information. DAML supports principled action selection in norm-governed multi-agent systems, and is the first such framework to derive these obligations using the action model logic machinery. We provide an axiomatization of the logic and prove soundness and completeness with respect to its semantics. Finally, we demonstrate the expressive power of our framework through applications to the Miners' Puzzle and other multi-agent deontic scenarios.

Higher order cumulants between the mean transverse momentum and elliptic flow are calculated in a relativistic viscous hydrodynamic model of relativistic heavy-ion collisions. The results of the hydrodynamic simulations are compared with calculations using event-by-event predictors of the final collective observables constructed from the initial state entropy distribution. The predictors describes quantitatively centrality dependence of the higher cumulants considered in the paper. We derive a quantitative relations between the cumulants of the mean transverse momentum and different moments of the harmonic flow. The hydrodynamic simulations satisfy those relation very well. Those relations could be used to test experimentally the collective origin of the observed correlations between the mean transverse momentum and harmonic flow.

Infrared spectroscopy provides a powerful diagnostic for probing the mineralogical properties of dust grains in the terrestrial planet-forming regions of protoplanetary disks. The Upper Scorpius association offers an excellent laboratory for studying disk evolution because it represents an evolved stage (5-10 Myr) compared with younger star-forming regions such as the Taurus Molecular Cloud (1-3 Myr). In this work, we present mid-infrared spectra of 11 disks in Upper Scorpius that were obtained with the Mid-Infrared Instrument aboard the James Webb Space Telescope. We derive emission feature indices for crystalline olivine and pyroxene centered at about 9.2 micron and 11.1 micron, as well as perform spectral decomposition to quantify dust crystallinity and characteristic grain size. These results are compared with those measured from Spitzer/IRS spectra of 31 disks in Taurus with similar stellar types. We find no significant difference in dust crystallinity between the two groups, suggesting that crystallization is largely established at early stages of disk evolution. Our analysis indicates that the average grain size in Upper Scorpius disks is systematically larger than that in Taurus disks, aligning with theories of dust evolution. We also observe a trend of increasing grain size towards later-type stars, as well as a correlation between crystallinity, grain size and the flux ratio F24/F8, which serves as a measure of dust settling. These results suggest that dust processing proceeds in tandem with disk evolution.

We investigate the attractor of spin density in relativistic spin hydrodynamics using Zubarev's non-equilibrium statistical operator formalism in the spin probe limit. We derive the (0+1)D Bjorken flow equations and the associated attractor equation while retaining second order gradient corrections in the relevant relaxation constitutive equations including couplings associated with nonlinear response and nonlocal memory effects. We analyze the early time fixed point structure and analytically determine the early time attractor solution, thereby clarifying branch selection and the role of different dynamical corrections. We find that source-like driving terms modify the leading correction to the attractor solution without changing the fixed point structure, whereas self feedback terms involving the rotational stress tensor modify the dominant balance and modify the early time fixed point structure. We further study the late time asymptotic behavior in the conformal limit and show that the newly added terms affect the first subleading asymptotics without changing the leading late time branches. These results provide a unified picture of early and late time attractor dynamics in the conformal limit.

Operando microscopy has unveiled key mechanistic insights in battery materials during early cycling, but long-term characterization to unveil material evolution, degradation, and failure remain limited. To address this gap, we develop a custom operando optical microscope that captures images across hundreds of cycles and hours using optically accessible, anode-free pouch cells. We image through-plane, bulk-representative electrodeposition behavior of aqueous tin metal anodes, which are promising due to their high energy density but whose reactivity limits practical cycle life. We show that substrate governs the morphology and stability of plated tin, particularly at high plated capacities. Specifically, copper substrates exhibit a multi-stage tin growth mode, which results in high overpotentials and irreversible active material loss at high plated capacities. In contrast, graphite substrates display a single-stage growth mode with slower kinetics. Using this insight, we balance performance and stability to demonstrate a high-utilization (70%, 630 mAh g$^{-1}_{Sn}$) porous graphite substrate Sn anode with high efficiency and long lifetime. Our results underscore the importance of material and device optimization guided by operando characterization across device lifetime with broad applicability to electrochemical systems.

The neutrinos produced in $τ$ decays cannot be directly detected, making the reconstruction of $τ$ kinematics challenging and affecting measurements of quantum correlations such as spin entanglement. For the process $e^+e^- \to τ^+τ^- \to π^+ \barν_τπ^-ν_τ$, the kinematic constraints allow the $τ$ momenta to be reconstructed up to a well-known two-fold ambiguity, regardless of the presence of an intermediate resonance state. In this paper, we present a geometric interpretation of this ambiguity and propose a numerical reconstruction method based on singular value decomposition (SVD). Using only the information from visible final-state particles and decay kinematics, the method reconstructs the two possible solutions for the $τ^+τ^-$ pair. The reconstruction performance is validated with Monte Carlo simulations in typical collider environments. We further investigate the impact of the spurious solution on spin-entanglement measurements and show that reliable entanglement signals can still be extracted even when the true and spurious solutions cannot be experimentally distinguished. This work provides a practical approach for $τ$-lepton kinematic reconstruction and spin-entanglement measurements in $e^+e^-$ collider experiments.

For Huber contamination on a known finite sample space, the unrestricted contaminating law is a probability vector on the support atoms, and domination over all measurable subsets reduces to atomwise inequalities. Placing a Dirichlet prior on this probability vector and a Beta prior on the contamination proportion gives an exact marginal likelihood for the structural parameter after analytic integration of both nuisance quantities. The likelihood is a finite weighted sum over allocations of the observed counts between the structural and contaminating components. For fixed support size, this sum and its score can be evaluated by a dynamic program with quadratic cost in the sample size, enabling gradient-based posterior sampling.

The main goal of this work is to quantify, through a robust methodology, the contribution of invariant manifolds to the formation of spiral arms in a pure N-body simulation, setting up a machinery to perform similar tests in other and more complex simulations. We computed the invariant manifolds associated with the hyperbolic equilibrium points of the effective potential and quantified the fraction of particles whose motion is governed by these phase-space structures. We then compared the temporal evolution of this trapped fraction with the strength of the spiral arms, traced by the A2 Fourier amplitude. We find a correlation between the fraction of trapped particles in the unstable exterior branches of the invariant manifolds and the strength of spiral arms. In particular, we determine that up to 50% of all the particles located on the spiral arms region (and up to 90% from the manifold-compatible population) are trapped by the manifolds, with oscillations of period of the order of 100 Myr. Invariant manifolds provide a dynamically relevant framework for understanding the formation of spiral structure in pure N-body simulations of barred galaxies. We present the first quantitative evidence, based on a fully self-consistent N-body model, that a significant fraction of spiral-arm particles is governed by manifold-driven dynamics. These particles act as seeds of overdensities that subsequently evolve into fully developed spiral arms through the delayed gravitational response of the disc to the self-gravity of the manifold-trapped material. The influence of the invariant manifolds remains non-negligible at all times, and phases of stronger spiral structure are associated with higher trapped fractions.

This paper investigates a multidimensional non-homogeneous stochastic linear-quadratic optimal control problem featuring random coefficients and a terminal mean-field term in the cost functional, enabling its direct application to mean-variance models in financial engineering. Employing the Lagrangian duality method together with a decomposition approach for linear backward stochastic differential equations, we provide two types of sufficient conditions for solvability and derive the corresponding optimal controls. In particular, in the deterministic-coefficient case, our condition is weaker than the standard condition found in the existing literature on mean-field stochastic LQ problems. Finally, a numerical example drawn from optimal portfolio selection with multiple assets under mean-variance utility demonstrates the applicability of our results.

The increasing densification of small-cell networks substantially expands cable-based backhaul infrastructure, creating heightened vulnerability to cable link failures. This paper proposes a reconfigurable intelligent surface (RIS)-assisted backup framework that exploits a key insight: during backhaul cable failures, base station (BS) radio components remain functional, enabling wireless backhaul traffic redistribution. Our framework maintains network connectivity by redistributing disconnected BS backhaul traffic to neighboring BSs through RIS-assisted wireless links. To maximize survivability across varying traffic conditions, we formulate a joint optimization problem that maximizes total resolvable backhaul traffic by jointly deciding BS selection, RIS phase shifts, and precoding vectors. The inherent non-convexity arising from coupling and quadratic fractional term is addressed through an alternating optimization algorithm that iteratively solves tractable convex subproblems via quadratic transformation. Comprehensive numerical evaluations demonstrate that the proposed RIS-enhanced framework significantly improves survivability from 58% to 72% under challenging high-intensity hotspot traffic conditions. Moreover, RIS provides the greatest gains for antenna-constrained systems by extending coverage to access more spare capacity of the distant BSs as well as enhancing the signal strength. Consequently, high survivability is achieved even with only two antennas per BS under moderate traffic intensity.

Pinching antenna systems (PASS) enable reconfigurable radiating elements and extended line-of-sight communication, mitigating path loss effects. However, existing designs lack fully controllable radiation weights, as they are governed by structural parameters rather than explicitly assigned variables. In this paper, we introduce a new degree of freedom (DoF) for PASS by enabling radiation weight control through phase-mismatch manipulation of guided waves under single-mode excitation within a coupled-mode framework. By tuning the propagation constants of pinching antennas, independent complex-weight control of individual elements is achieved, transforming PASS into a weight-adaptive analog beamforming architecture. Based on this principle, we present a physics-based hardware model that provides a unified framework for both amplitude-tunable pinching beamforming and conventional equal-power radiation models, ensuring compatibility with existing PASS implementations, such as movable setups. To evaluate the proposed model, we formulate a sum-rate maximization problem for hybrid precoding in multiuser downlink systems and solve it using an alternating optimization framework that combines weighted minimum mean square error-based digital precoding with genetic algorithm-based optimization of PASS configurations, including various scenarios such as weight tuning, antenna movability, and discrete activation. Numerical results demonstrate that the amplitude-tunable PASS architecture achieves consistent performance gains over conventional arrays and existing PASS schemes, with pronounced improvements in interference-limited regimes under practical constraints.

The nonlinear Breit-Wheeler process is a fundamental phenomenon of strong-field quantum electrodynamics and is usually studied for classically prescribed laser backgrounds. Here we examine how the statistical properties of a squeezed coherent driving field modify nonlinear Breit-Wheeler pair production. Using a polarization-resolved Monte Carlo framework with stochastic averaging over the field-amplitude distribution derived from the Husimi Q-function, we simulate collisions of gamma photons with squeezed light and identify clear source-state-dependent modifications of the pair production signal. These effects include the smoothing of harmonic structure, the enhancement of higher-order multiphoton channels, and the suppression of the single-laser-photon absorption channel when stronger-field realizations raise the dressed-mass threshold. Within the selected spectral window, the degree of positron polarization increases monotonically with the squeezing parameter, while the angular distributions broaden as the statistical weight of larger field amplitudes increases. Our results show that, even at fixed mean electric-field amplitude, the statistical fluctuations inherent to the squeezed coherent state can substantially reshape spectral, angular, and spin-resolved observables in strong-field pair production. These findings illustrate a direct link between source-state-dependent field statistics and strong-field pair production observables, and provide a theoretical framework for studying how squeezed-state preparation of the driving field can influence high-energy QED processes.

Clock synchronization is critical for maintaining low error rates in quantum key distribution. Here, we describe how a frequency mismatch between the transmitter and receiver clocks affects the quantum bit error rate in quantum key distribution, and derive from this model a simple synchronization algorithm together with clock stability requirements for practical operation. Our algorithm continuously compensates for both frequency mismatch and time-offset fluctuations directly from detection timestamps. It does not require a dedicated synchronization channel or auxiliary qubit sequence, converges from a large frequency mismatch within approximately one second of photon acquisition, and remains effective in low-photon-count regimes (more than 30 dB of channel loss) using standard hardware. We validate our approach by demonstrating successful key exchange over 100 km of fiber and continuous operation over 24 hours in a 16 km metropolitan network using commercial systems, with performance equivalent to using a service channel for clock synchronization.

Ablation with mixed $2ω$--$3ω$ lasers is investigated as a possible drive strategy for balancing drive efficiency and ablative stabilization in direct-drive inertial confinement fusion. One-dimensional radiation-hydrodynamic simulations are performed for planar CH targets using the FLASH code [B. Fryxell et al, The Astrophysical Journal Supplement Series \textbf{131}, 273 (2000)]. The total target-incident laser intensity is varied from 100 to $1600~\mathrm{TW}/\mathrm{cm}^{2}$, and the $3ω$ laser intensity fraction is scanned from 0 to 100\%. Thick-target simulations are used to determine quasi-steady ablation-pressure scalings, while thin-foil simulations are used to characterize the acceleration stage and to evaluate the linear ablative Rayleigh--Taylor instability (RTI) gain using a Takabe-type model. The simulations show that adding a $3ω$ component to a $2ω$-dominated drive increases the effective ablation pressure, enhances the ablation velocity, and reduces the maximum linear RTI gain. Within the present one-dimensional hydrodynamic model, the mixed drive also reduces the target-incident energy required to accelerate the foil to $300~\mathrm{km}/\mathrm{s}$, especially at high intensity. This improvement is attributed to the deeper penetration of $3ω$ light, which deposits energy closer to the dense ablation region and enhances conductive heat transport toward the ablation front. These results suggest that mixed-wavelength drive can recover much of the favorable hydrodynamic performance of $3ω$ irradiation while retaining part of the energy-accessibility advantage of $2ω$ operation, providing an additional design space of freedom for direct-drive target optimization.

Let $X$ be a complex space of pure-dimension $n$. For a pseudoconvex relatively compact domain in $X$ with $\mathscr{C}^3$-smooth boundary and embedded in a domain of the complex number space, we prove that the $L^2$- and $L^{2,\mathrm{loc}}$-Dolbeault $(n,q)$-cohomology groups are vanishing for $q>0$. Thereby, we include the case that the forms have values in a Nakano semi-positive holomorphic vector bundle. Using this local vanishing theorem, we also prove the equivalence of the $L^2$- and $L^{2,\mathrm{loc}}$-Dolbeault $(n,q)$-cohomology groups of domains $Ω=\{ρ<0\}$ in $X$ which are defined by a $\mathscr{C}^3$-smooth function $ρ$ which is strictly plurisubharmonic on a neighbourhood of $\partialΩ$ except of finitely many points.

Anthropogenic underwater noise from offshore wind turbines is a growing environmental concern, particularly with the large-scale deployment of bottom-fixed and floating devices. This study presents a physics-based vibroacoustic framework to predict operational underwater noise emissions from offshore wind turbines and compares monopile-supported and floating configurations for a 10 MW turbine. The methodology combines time-domain aero-hydro-servo-elastic simulations with a frequency-domain acoustic formulation based on equivalent dipole sources and Green's function solutions, accounting for underwater confinement between the free surface and seabed through the method of images. Results show that floating configurations exhibit enhanced low-frequency acoustic emissions, producing up to 15% higher OASPL than the monopile structures under equivalent water depths for frequencies below 10 Hz due to additional rigid-body motions, while monopile structures radiate more efficiently at higher frequencies associated with drivetrain excitations. Significant differences in the spatial distribution and directivity of the radiated sound field are also observed, with floating platforms displaying more complex three-dimensional radiation patterns and stronger direction-dependent variations, reaching approximately 20-25 dB in the 100-1000 Hz band, compared with the smoother and nearly axisymmetric response of monopile configurations. Water depth strongly influences propagation regimes and overall sound levels, with shallow-water floating configurations showing variations of up to 7% in OASPL relative to deep-water cases. The proposed framework enables quantification of vibro-acoustic noise and provides a predictive tool for assessing underwater acoustic impacts during the design phase, supporting environmentally informed offshore wind turbine design and future regulatory and monitoring strategies.