Quantum algorithms for integer factorization represent one of the most prominent applications of quantum computation, with far-reaching implications for modern cryptography. While Shor's algorithm provides a polynomial-time solution in the ideal quantum model, its practical implementation is severely constrained by the limitations of current noisy intermediate-scale quantum (NISQ) hardware. These constraints have motivated the exploration of alternative factoring algorithms with different structural and resource trade-offs. In this work, we present an experimental study of Regev's quantum factoring algorithm, implemented on real quantum hardware, and compare its behavior with that of Shor's algorithm under analogous conditions. Focusing on the case N = 15, we execute both algorithms on the QMIO quantum computer at the Centro de Supercomputacion de Galicia (CESGA) and contrast the results with one of IBM's open-access quantum computers and ideal simulations. This parallel execution enables a low-level comparison of the two algorithms, highlighting how their respective quantum implementations interact with hardware noise, limited circuit depth, and finite sampling. Our analysis emphasizes the different ways in which Shor's and Regev's algorithms encode arithmetic structure into quantum states through Fourier sampling in one and higher dimensions, respectively, and how these differences manifest in experimental outcomes. Although neither algorithm demonstrates a practical advantage in the small N regime, the results provide insight into their relative robustness and failure modes on contemporary quantum devices. This study illustrates the value of experimental benchmarking of alternative quantum factoring algorithms as a means of understanding the practical implications of algorithmic design choices in the NISQ era.

To enhance the way in which wave-particle duality is implemented in the modelling of quantum mechanical systems, Bukhari et al. [New J. Phys. 27, 084501 (2025)] recently introduced an alternative approach to quantum mechanics, namely quantum mechanics in configuration space. This formalism is based on a physically motivated quantisation of Newtonian mechanics and promotes the classical position-velocity states (x,v) to pairwise distinguishable quantum states. The resulting |x,v> states form the basis of the Hilbert space of individual quantum mechanical particles and evolve along classical trajectories. In this paper, we consider the modelling of a mechanical particle in free space and put quantum mechanics in configuration space into context. It is shown that this formalism increases the continuity between quantum and classical mechanics by avoiding a conceptual inconsistency associated with the definition of momentum in canonical quantisation. In addition, we emphasise that standard quantum mechanics and quantum mechanics in configuration space are based on two distinct formulations of classical mechanics.

Diagonal unitaries are a fundamental but resource-intensive class of quantum operations, arising as the phase separators of QAOA and the time-evolution blocks of Hamiltonian simulation. Under all-to-all connectivity their optimal depth is established, but on nearest-neighbor hardware general-purpose compilers fall back on heuristic search, which yields no analyzable cost bound and becomes intractable at the very sizes where depth is the bottleneck. We address synthesis and compilation jointly. On the synthesis side, we develop a Gray-Path Framework (GPF) that realizes any $n$-qubit diagonal unitary in asymptotically optimal $R_z$ and CNOT depth $O(2^n/n)$ without ancillas. Our main result is that compiling GPF onto a two-dimensional nearest-neighbor grid preserves this optimality: routing adds depth $Θ(2^n/n)$ and gate count $Θ(2^n)$. Because GPF fixes its entire interaction structure in advance, routing reduces to scheduling a known sequence, with no heuristic search. We give the construction both with and without ancillas: the ancilla-free, cost-optimized layout is a two-row grid, and a $2k$-row layout introduces a space--time tradeoff that cuts depth by $1/k$ while remaining asymptotically optimal for the enlarged register; both are deterministic and analyzed in closed form. The same complexity is also attained on a linear nearest-neighbor chain, so the preservation is topology-independent, holding on any architecture that contains such a chain. All routing bounds are closed-form, giving the concrete resource estimates that heuristic compilers cannot provide at scale.

As we move towards scalable and modular quantum computing, quantum data centres become imperative. Existing analyses typically treat network constraints in isolation or through simplified models, leaving the interplay between error correction operations and communication resources underexplored. In this work, we present an end-to-end simulation framework that jointly models surface-code operations, internal QPU connectivity, and realistic network constraints including finite entanglement generation rates, limited communication qubits, and bandwidth contention, producing execution latency, from which logical error rate estimates are obtained. The framework is modular by design, allowing individual components such as routing heuristics, scheduling policies, and network topologies to be independently replaced. Numerical evaluation reveals distinct operating regimes in which the optimal resource allocation and code distance selection shift depending on the network characteristics. These results point to tradeoffs in the design of distributed quantum computing architectures that are not visible when computation and communication are modeled separately.

Daylight entanglement-based free-space quantum key distribution (QKD) is limited by accidental coincidences from receiver-admitted background light. We develop and experimentally validate a receiver-level framework linking receiver bandwidth, accepted temporal width, and background-noise density to Bob singles, sifted-key rate, error rate, and quantum bit error rate (QBER) in telecom-wavelength BBM92 QKD. Indoor sweeps show that useful sifted counts saturate near the source-matched bandwidth, whereas broader bandwidth or higher background mainly increases accidental contamination. Increasing the accepted temporal width leaves Bob singles nearly unchanged but directly raises QBER by enlarging the random-overlap probability. A two-dimensional design map shows that the temporal-window margin contracts rapidly with increasing background-to-signal ratio, while the bandwidth margin remains comparatively broad near source-matched filtering. A 10 m rooftop daylight experiment demonstrates operation in the predicted low-accidental regime, yielding a mean sifted-key rate of 2,811 cps and a mean QBER of 4.43%.

Useful chemistry calculations on near-term quantum processors are hindered by current algorithmic runtimes. We develop a methodology to significantly reduce these runtimes. Typically, variational quantum eigensolver (VQE) algorithms are implemented as sequences of primitive gates. Our methodology instead relies on gradient-ascent pulse engineering to construct hardware-tailored pulses for the direct implementation of VQEs. As problem sizes increase, it quickly becomes intractable to optimise a pulse that implements an entire VQE ansatz circuit. However, leading VQEs are constructed in a modular fashion. A problem-tailored VQE is assembled from parameterised circuit elements that simulate hopping between two or four electronic spin orbitals. We show that these circuit elements can be implemented more efficiently using hardware-tailored pulses. We numerically demonstrate our methodology on a silicon spin-qubit quantum processor. We find that common circuit elements, known as single- and double-qubit excitations, can be implemented in less than 289 ns and 927 ns, respectively. Compared with conventional gate-based implementations, our pulse-accelerated qubit excitations provide a scalable approach for faster and therefore more noise-robust quantum chemistry simulations by reducing VQE runtimes by up to a factor of 15.3.

Generalizing the construction of two-block group algebra (2BGA) codes, we introduce a family of two-block quantum LDPC codes constructed using the action of a group on the cosets of its subgroup. This replaces the regular group actions of the earlier two-block constructions and significantly expands the search space, yielding new quantum LDPC codes outside the 2BGA family. Through a computer search, we identify several new quantum LDPC codes, including weight-6 codes with parameters $[[48,8,6]]$, $[[96,8,10]]$, and $[[224,12,16]]$, as well as weight-8 codes with parameters $[[84,16,8]]$, $[[112,16,10]]$, $[[128,16,12]]$, and $[[168,16,15]]$. Furthermore, we introduce a maximally packed syndrome extraction schedule of depth $w+2$, including initialization and measurement steps, for any code with a maximum stabilizer weight of $w$ from our family. Under a standard circuit-level noise model, our codes, when decoded using BP-OSD, perform competitively with BB codes, achieving thresholds of $\approx0.65\%$ for the weight-6 family and $\approx0.35\%$ for the weight-8 family. Finally, we introduce a group-theoretic framework to generate sequences of graph-based covers of 2BGA codes, recovering and extending recent results on code constructions of this type.

Learned PDE simulators are increasingly used as low-cost replacements for expensive numerical solvers, but standard relative $L^2$ error does not determine whether a learned model behaves as a coherent numerical time propagator. This paper presents a diagnostic software suite for auditing learned PDE simulators as approximate evolution operators. The suite provides architecture-independent, post hoc diagnostics for relative state error, semigroup consistency, finite-difference generator discrepancy, energy behavior, integral balance, admissibility constraints, perturbation response, and scaling-law consistency. The software is designed around a minimal contract: reference trajectories, a learned propagator or saved predictions, equation metadata, and a diagnostic configuration specifying which structures are meaningful for the problem under study. We validate the suite on five benchmark PDE tasks: two-dimensional incompressible Navier-Stokes, shallow-water dynamics, active matter, three-dimensional compressible Navier-Stokes, and three-dimensional magnetohydrodynamics, using FNO, DeepONet, U-Net, and ResNet-style surrogate models together with controlled underfit and oversmoothed variants. The validation study shows that relative $L^2$ error can remain moderate, or even improve, while structural diagnostics deteriorate substantially. The package therefore supports software-level auditing of learned PDE simulators by reporting an interpretable diagnostic panel rather than collapsing model behavior into a single state-error score.

A fourth-order conservative adaptive multiresolution average-interpolating wavelet upwind scheme is proposed for compressible flows governed by hyperbolic conservation laws. A family of asymmetric average-interpolating wavelets with upwind properties is constructed for conservative finite volume discretization, while symmetric average-interpolating wavelets are employed for multiresolution decomposition and reconstruction of physical variables in the adaptive procedure. Since both the conservative discretization and the adaptive multiresolution representation are constructed from cell-average quantities, the proposed scheme preserves strict conservation during both numerical evolution and adaptive cell redistribution. Unlike hybrid adaptive wavelet methods that use wavelets mainly for data compression and mesh adaptation, the present adaptive wavelet upwind scheme utilizes average-interpolating wavelet multiresolution approximation to reconstruct the interface values directly for numerical flux evaluation, thereby avoiding additional ghost-cell marking and reconstruction near coarse--fine mesh interfaces. The boundary variation diminishing reconstruction is incorporated at the finest resolution level to achieve non-oscillatory shock-capturing capability. Numerical tests demonstrate that the proposed scheme achieves the expected fourth-order accuracy, maintains conservation errors close to machine precision, and controls numerical errors around the prescribed threshold. The proposed method also sharply captures shock waves and contact discontinuities without spurious oscillations and resolves multiscale smooth structures through a sparse adaptive representation. These results indicate that the proposed scheme provides an efficient, conservative, and reliable approach for high-resolution simulations of compressible flows.

Photon anti-bunching is the direct evidence for the existence of photons without having a classical counterpart. Unlike bunching of photons, which can have a semi-classical description, the effect of photon anti-bunching can only be understood with quantized electromagnetic fields. However, for the process of high harmonic generation (HHG), where many photons of the driving field are upconverted to a single photon of higher energy, there is yet no clear evidence for the presence of individual photon emission. The key result of this work is the prediction of photon anti-bunching in the process of HHG, marking it the first theoretical discovery of non-classicality in the temporal correlations of HHG photons. While other non-classical signatures in HHG, such as sub-Poissonian statistics or squeezing, have been discussed for an ensemble of photons, the anti-bunching signature reported here is a signature of a single photon. This is achieved by using the recently developed Heisenberg picture approach for quantum optical HHG, revealing clear anti-bunching signatures in the intensity correlation function across the entire harmonic spectrum.

We demonstrate broadband phase-sensitive amplification (PSA) measurement of squeezed light generated by a waveguide optical parametric amplifier (OPA) with external dispersion compensation. In broadband systems, group velocity dispersion (GVD) induces a frequency-dependent rotation of the squeezing axis, which limits the observable bandwidth in PSA measurements. To overcome this limitation, we introduce external dispersion compensation between two OPAs and suppress the quadrature rotation over a wide frequency range. As a result, we observe a maximum squeezing of 5.9 dB near the carrier frequency and more than 5 dB of squeezing up to a frequency offset of 4.5 THz from the carrier. Furthermore, squeezing below the shot-noise level is confirmed up to a frequency offset of 6 THz from the carrier, corresponding to the accessible phase-matching bandwidth of the waveguide OPA. Our results establish a practical method for broadband characterization of squeezed light and provide a key step toward ultrafast continuous-variable quantum information processing.

Distributed entanglement across multi-node quantum networks is essential for a wide range of quantum technologies, including modular quantum computers, distributed sensing and metrology, and multi-party secure communication protocols. Such large-scale quantum networks will require photonic interconnects to generate and sustain entangled states across localized nodes. Previously, three-node distributed Greenberger-Horne-Zeilinger (GHZ) states have been generated between solid-state qubits and atomic ensembles, but not yet in the platform of individual atomic qubits, which can be replicated, detected, and individually controlled with high fidelity. Here we report the first fully-distributed GHZ state of qubits across a three-node quantum network of single atomic memories, using photonic interconnects. We achieve a bounded fidelity of $0.841(17) \leq \mathcal{F} \leq 0.881(17)$ at an entanglement generation rate of 0.095(5)/sec and measure a clear violation of Mermin's inequality while closing the detection loophole for the first time in a fully-distributed multipartite entangled state.

A fluid system is 'globally stable' if all initial conditions eventually converge to the same state. Since Reynolds (1895) and Orr (1907), the standard way to show global stability has been the energy method, which uses the fluctuation energy as a Lyapunov function. However, the energy method fails whenever transient energy growth is possible, so it often yields overly strict stability criteria. The first broadly applicable alternative has recently been introduced (Goulart & Chernyshenko 2012; Fuentes et al. 2022), using polynomial optimization to construct non-quadratic Lyapunov functions. Unlike the energy method, however, this approach is highly technical, computationally expensive, and hard to interpret physically. Moreover, it treats only one set of parameters at a time; in particular, if it verifies global stability at a certain Reynolds number, it does not imply the same for smaller values. The present work makes progress by connecting this numerical program with new analytical and physical insights. We show how to exploit symmetries of shear flows via convenient complex variable representations, greatly reducing the problem size. We then refine key inequalities, replace several expensive computational steps with simpler analytical alternatives, and show how to prove global stability over a range of Reynolds numbers. Our analysis identifies the simplest class of non-quadratic Lyapunov functions for two-dimensional parallel shear flows: a three-parameter family of quartic polynomials. Using these Lyapunov functions, we verify global stability of 2-D plane Couette flow and plane Poiseuille flow up to higher Reynolds numbers than possible with the energy method. Our work takes a step towards an analytical theory of global fluid stability beyond the energy method, and offers structural insights that should significantly improve future numerical investigations of global stability.

Political polarization poses a variety of challenges for modern democracies. Entrenched disagreements on policy can prevent constructive discourse and compromise, and high levels of affective polarization threaten to undermine social cohesion and support for institutions. Finding ways to promote constructive discourse while maintaining free expression has proved a challenge for social media platforms, media outlets and policy makers alike. Here we develop a computational model -- based in psychology -- of online discourse and opinion dynamics under complex individual identities, which we use to assess the capacity of realistic interventions to prevent or reverse polarization. We show that changes to the range of acceptable opinions in a society -- i.e. the Overton window -- have a limited impact on polarization, and that attempts to ``optimize'' the Overton window can even trigger the onset of polarization. In contrast, interventions that shift attention towards under-discussed topics, or increase the costs of violating existing norms, are often effective at preventing polarization, but are less successful at reversing it. Most strikingly, increasing the salience of influential individuals, who model non-polarized discourse, can be highly effective at both preventing and reversing polarization. However we also find that once polarization has set in, even the most successful interventions result in latent extremism when identities are complex. Our work suggests that restricting speech by shrinking the range of acceptable discourse is an ineffective way to tackle polarization, whereas enforcement of existing norms, attention nudges and the presence of elites who model good behavior can be highly effective.

Physics-informed neural networks (PINNs) offer a promising framework for inverse hydrodynamic modeling by combining sparse observations with governing physical constraints. However, their reliability for estimating hydraulic parameters under data limitations remains insufficiently characterized. This study benchmarks inverse PINN recovery of the Manning friction coefficient in the shallow water equations under controlled variations in observation sparsity, noise, and observed variable type. Two cases are considered: a one-dimensional MacDonald subcritical channel with an analytical steady reference solution, and a two-dimensional sloped channel with a parabolic transverse bed generated using a balanced finite-volume solver. The Manning coefficient is treated as a trainable positive scalar and recovered jointly with the flow field using a two-phase strategy that first fits observations and then incorporates the physics residual. Results show that the two-dimensional case achieves robust friction recovery, with errors below 5% when at least 10 depth and velocity observations are available and noise is at or below 10% of the field standard deviation. Recovery remains stable up to 20% noise with 50 observations, but becomes unreliable with only five observations. In contrast, the one-dimensional case shows a persistent positive bias of about 15% that is largely insensitive to observation count and noise, indicating a structural identifiability limitation rather than a data-density limitation. Observation-type ablation shows that recovery degrades substantially when only depth or velocity is observed, demonstrating that joint depth-velocity information is essential for reliable inverse identification. Overall, the results provide a reproducible benchmark for assessing when inverse PINNs can and cannot reliably estimate Manning friction from sparse and noisy shallow-water observations.

Electromagnetic finite element method (FEM) implementations using traditional basis functions struggle to accurately represent field behavior near singular features such as conducting wedges. To combat this, specialized singular basis functions have been introduced to directly model the singular fields in these regions, leading to substantially improved performance. While these efforts have been pursued extensively in 2D, few functions have been developed for 3D elements. In this work, we develop basis functions for this in tetrahedra. Unlike prior functions, these basis functions are additive, meaning they are included alongside the standard vector basis functions to achieve more robust performance. Further, these functions are designed to be adaptable to tetrahedra touching several unique singular features by using combinations of basis functions singular with respect to each node and edge in the element, making them applicable to highly complex geometries. Higher-order interpolatory versions of the basis functions for modeling singular behavior with greater accuracy are also provided. These basis functions lead to substantial improvements in accuracy relative to the standard basis functions, and allow otherwise expensive simulations to be performed at far lower costs. As an application example, we perform simulations to extract critical quantities for designing superconducting qubits that significantly depend on the behavior of singular fields. In Ansys HFSS, this took 21.27 hours and a peak memory usage of 6.23 TB with 800 processors available, while using our singular basis functions achieved comparable results in 196 seconds while using 27.24 GB of memory and only 16 processors. Due to these benefits, our singular basis functions could be applied to enable design optimization of electromagnetic geometries with dominantly singular behavior, such as superconducting qubits.

Chip-based nonlinear photonics offer the capability to integrate devices with all the requisite photonic components (e.g., filters, couplers, detectors) into highly compact form factors. This offers the possibility of making the devices scalable, robust, and manufacturable. Such integrated photonic devices will enable applications in optical communications, precision metrology, microwave generation, and LIDAR. However, thermal instabilities represent a major hurdle in the deterministic operation of nonlinear optical processes in such integrated resonant structures such as microresonators. In this work we demonstrate deterministic and highly stable Kerr soliton comb generation in tight-spiral microresonators with comb spacings as low as 16 GHz. We perform a comprehensive experimentally-validated thermal model of such compact microresonators and reveal non-trivial thermally-driven instabilities governing the cavity soliton dynamics. We design and implement a fast feedback loop on the devices to overcome thermal perturbations and stabilize cavity-soliton states, including those that are otherwise unstable, and to allow for controlled transitions between the soliton states. Our approach enables the realization of thermally-stable highly compact soliton microcomb devices in a wide variety of photonic platforms.

MERMAID-v1 is a prototype PET scanner designed to support biomedical research involving adult zebrafish and similar species. The current experimental setup has been characterized, and scans of various phantoms, as well as adult zebrafish have been conducted. A dedicated reconstruction software was implemented, including accurate modeling of the parallax effect. The average energy resolution was 21.6% (FWHM at 511keV), with no significant dead-time effects observed for activities up to 18MBq. The absolute sensitivity at the center of the field of view (FOV) ranged from 0.06% to 0.31%, depending on the energy window (from 450-550 to 300-600keV), reflecting the limitations of the current two-head configuration. In the central 12mm of the transaxial FOV, the averaged spatial resolution is approximately 0.77mm (FWHM) transaxially and 0.66mm axially, as evaluated using a point source. Image quality was assessed using a downscaled NEMA-inspired IQ phantom and a 3D-printed Derenzo phantom. The reconstructed images suggest a spatial resolution around 0.7mm - 0.8mm, despite the lack of depth-of-interaction information. The first ex- and in-vivo PET scans of adult zebrafish were successfully performed, showing detectable tracer uptake in organs such as the brain and eyes despite low initial activity levels. These results confirm MERMAID-v1 capability to obtain useful results from the acquired data from living, anesthetized fish in a water-filled imaging chamber. While no scatter, attenuation, or efficiency corrections have yet been implemented, this work establishes a working proof-of-concept for dedicated PET imaging of small aquatic vertebrates. Future developments will focus on developing correction techniques, expanding the detector array, and integrating complementary modalities such as CT.

Quasi-optical coupling serves as the critical interface in terahertz (THz) heterodyne receiver systems, enabling efficient transfer of incident radiation to superconducting hot-electron bolometer (HEB) mixers through a focusing element and a planar microwave antenna. With recent advances in nanofabrication, planar dielectric metalenses have emerged as promising alternatives to conventional refractive optics due to their compactness and scalability. However, unlike conventional elliptical silicon lenses that are often treated as nearly ideal optical components, the focusing efficiency of metalenses is strongly dependent on the local deflection angle across the aperture, creating an urgent need to quantitatively understand the coupling between a dielectric metalens and a planar antenna. In this work, we present a quasi-optical coupling analysis between a planar Si metalens and a logarithmic spiral antenna integrated with a THz superconducting NbN HEB mixer operating at 1.63 THz using a spherical-coordinate vectorial integration. By combining the angular radiation profile of the spiral antenna with the deflection-angle-dependent focusing efficiency of the metalens obtained from numerical simulations, the calculated coupling efficiency is directly correlated with experimentally measured double-side-band receiver noise temperatures through comparison with a conventional elliptical Si lens measured under the same receiver configuration. The analysis establishes a quantitative relationship between metalens focusing efficiency, antenna coupling, and receiver noise temperature, providing guidance for optimizing metalens design and improving the overall performance of metalens-integrated THz heterodyne receivers.

The optimised tokamak-stellarator hybrid concept (Henneberg and Plunk 2024) has the potential to combine tokamak and stellarator advantages to achieve magnetically confined fusion. These compact quasi-axisymmetric designs can have a low aspect ratio and large plasma volume, good particle confinement, and relatively simple coils. Previous work showed that such magnetic configurations can in principle be reproduced by a single type of non-planar "banana coil" alongside the conventional tokamak coilset (Henneberg and Plunk 2025). In this work, we optimise banana coils while also considering engineering constraints beyond simple geometric measures. We quantify the characteristic geometries of force-optimised banana coils and the magnetic fields they generate, and analyse the mechanisms by which forces may be reduced through optimisation.

Ion exchange membranes are useful for a wide range of applications, including water desalination, fuel cells, and aqueous batteries. Accordingly, a variety of models for ion exchange membranes has been proposed, each emphasizing different aspects that govern their static and dynamic properties. By reviewing these models, we identify key physical contributions and beneficial modeling strategies. Based on these insights, we derive a thermodynamically consistent model by combining mass-action site occupation with mean-field electrostatic interactions along the polymer backbone. In this derivation, we explicitly account for multicomponent electrolytes at elevated concentrations. The parameters of the resulting model relate closely to those of other models, but gain consistency and interpretability through the underlying derivation. A discussion of the model parameters highlights redundancies and linkages between quantities that are commonly treated independently. Comparison to experimental data shows that both static and dynamic membrane properties are reproduced with good accuracy by the presented model. This makes it a promising basis for theory-driven membrane optimization and supports the tailored design of ion exchange membranes for various technologies.

Many of the most powerful and elegant models in physics are grounded in symmetries. In electrodynamics, for example, geometric symmetries govern the observable effects of light-matter interactions. However, for man-made objects, exact symmetries are rarely met and tiny deviations are common. Nonetheless, even approximate symmetries keep many symmetry-derived rules effectively intact. However, as we will show here, this is not universally true. We demonstrate that an incremental violation of the symmetry of a carefully designed system can produce an optical response maximally different from the unbroken symmetry case. To do so, we exploit symmetry-protected quasi-bound states in the continuum (qBICs). Specifically, we design a four-fold rotationally symmetric metasurface composed of nearly dual-symmetric meta-atoms that supports a pair of spectrally aligned electric and magnetic qBICs. At normal incidence, symmetry forbids helicity-preserving reflection. However, for arbitrarily small deviations from normal incidence, the strong resonant enhancement associated with the qBICs overcomes the near-symmetry suppression and enables perfect helicity-preserving reflection. This rapidly emerging violation of symmetry-rules reveals a fundamental intricacy when it comes to treating near-symmetric systems. At the same time, our work opens the door to novel applications in metrology and sensing.

The crack closure effect of a low-alloyed steel subjected to low-cycle fatigue loading has been characterized at two different imposed strain amplitudes. Two techniques (non-continuous DIC H-DIC and DCPD) have been employed in this aim, leading to similar conclusions. Thus, it is shown that the crack does not remain completely closed during a part of the compressive portion of the fatigue cycle for both applied loadings. The crack opening strains, combined with the cyclic stress-strain curve of the material allowed to determine an equivalent cyclic opening stress. This confirmed that the crack opening stresses decrease with the applied maximum stress when subjected to tension-compression loading, as commonly found in the literature.

The strong electric fields from tightly-focused and ultrashort laser beams have always been discussed as a way to accelerate charged particles without any need for a medium or external cavity. Radially-polarized light is one way to do this, motivated by the emergence of longitudinal electrical fields with tight focusing. However, the laser pulse will generally quickly overtake the electrons under its influence, and every-other half-cycle will decelerate the electrons in effect partially reversing the acceleration. In this work we present the effect of optical aberrations, primarily spherical aberration, and how despite their purely spatial nature they can significantly optimize the net acceleration, and advantageously allow for longer pulses to drive this optical field-based process. We discuss the optical physics responsible for this increase in performance and find optimal aberration profiles using a stochastic algorithm.

In practical applications, compressible particle-laden flows in moving geometries involve complex, non-linear, and multi-scale inter-actions with turbulent structures. Resolving these dynamics numerically requires careful algorithmic treatment to accurately predict particle trajectories. This work presents a high-fidelity Euler-Lagrange framework that couples a high-order discontinuous Galerkin spectral element method for the continuous phase with a Lagrangian point-particle tracking scheme. To manage moving and deforming domains, the framework integrates two distinct mesh movement strategies: the arbitrary Lagrangian-Eulerian method for general mesh deformations such as time-resolved particle-induced surface deformations and its special interface case, the sliding mesh approach, uniquely suited for rigid rotational or translational movements. A primary focus is placed on tightly coupling the arbitrary Lagrangian-Eulerian formulation into the temporal evolution step by utilizing radial basis function morphing to capture the non-linear feedback loop between evolving surface topologies and the continuous phase. Concurrently, the framework ensures time- and high-order accurate coupling of the mesh movement algorithms with the dispersed phase. In particular, the proposed algorithm resolves the sliding mesh tracking problem by enforcing strict spatial and temporal accuracy as Lagrangian particles cross non-conforming grid interfaces between adjacent moving zones. The algorithms are rigorously validated against multiple benchmarks and subsequently applied to two challenging compressor rotor applications: the first focusing on solid-particle erosion, and the second featuring an upstream cylindrical wake generator.

This article proposes a novel frequency-locking method based on frequency-domain unbiased phase estimation (FDUPE) for high-precision frequency control. By performing weighted recombination of the acquired data followed by Fourier-transform processing, the phase at the center of the data segment can be estimated without bias, making the method suitable for frequency-locking applications. The principle of the proposed method is analyzed, and an electronic prototype is developed to experimentally validate its feasibility. In the prototype, analog-to-digital converters (ADCs) are used for signal digitization, and a field-programmable gate array (FPGA) is used to implement the FDUPE algorithm. A digital proportional-integral-derivative (PID) controller is also implemented on the FPGA to provide feedback for accurate frequency locking. In the experiment, a (10~\mathrm{MHz}) voltage-controlled oscillator (VCO) with a free-running Allan deviation of (1 \times 10^{-9}) at (1~\mathrm{s}) is used as the device under test (DUT), while a rubidium atomic clock with an Allan deviation of (2 \times 10^{-11}) at (1~\mathrm{s}) serves as the high-stability reference source. Experimental results show that the proposed system achieves excellent locking performance, reducing the standard deviation of frequency fluctuations from (12.75~\mathrm{mHz}) root-mean-square (rms) in the free-running state to (0.88~μ\mathrm{Hz}) rms after locking. Correspondingly, the Allan deviation at (10~\mathrm{s}) is reduced from (9.6 \times 10^{-10}) to (1.45 \times 10^{-14}), representing a five-order-of-magnitude improvement in frequency stability.

In order to understand the variations in fault systems throughout the seismogenic cycle, mechanical states and the complexities of seismic interactions must be considered. In this study, we present a data integration framework combining a 25-year seismic catalog with Equivalent Water Height (EWH) datasets from the GRACE-FO mission and two information-theoretic complexity measures (Shannon and Tsallis entropy) to examine spatiotemporal changes in the East Anatolian Fault System associated with the 2023 Kahramanmaraş earthquake doublet. The pre-rupture period exhibits a systematic increase in the entropy measures alongside a gradual decrease in EWH, suggesting a transition towards fault network criticality driven by segment fragmentation, long-range correlations, poroelastic contraction, fluid migration, and progressive stress accumulation. During the co-seismic phase, we observe an abrupt increase in entropy with a corresponding negative shift in EWH. In the post-seismic period, the persistence of elevated entropy and EWH anomalies indicates that the fault system remains in a non-equilibrium state dominated by aftershock clustering, fault zone damage, permeability changes, and viscoelastic relaxation. Additionally, structured computational workflows detailing these joint methodologies are provided via the Seismic Entropy Analysis (Algorithm 1) and the Relationship Between Tsallis q and Gutenberg-Richter b-value (Algorithm 2) pseudo-codes, facilitating the direct reproduction and regeneration of all results.

We experimentally demonstrate dual-line coherent detection using an optical frequency comb local oscillator, enabling large frequency offset tolerance with minimal additional signal processing. The proposed method achieves 200 GHz offset tolerance for 400 Gbit/s signals with low penalty, supporting uncooled, low-cost coherent transceivers.

This study proposes a method to assess the potential of pooled on-demand transit feeder services in urban areas where demand is not yet known. We introduce the fraction of demand, reflecting the probability that a resident will use the service. Demand is generated on the distribution of residents address points at varying demand fraction levels. Through simulations, we match travellers into pooled rides and evaluate the service potential using three performance indicators (KPIs). We observe how these KPIs change with varying demand fractions and identify the most promising hub for each area. By setting KPI thresholds, we select the optimal combination of area and hub that meets these thresholds at the lowest demand fraction. This approach provides municipalities with a structured tool for pre-deployment evaluation, helping them choose the most suitable areas for launching new services despite the absence of exact demand data. We illustrate its application through a case study in Krakow, ranking 12 pre-selected areas for feeder service deployment.

The dynamic Leidenfrost effect LFE and behaviour of impinging colloidal droplets is strongly influenced by the impact and spreading paradigms. LFE actuated rebound and levitation occurs due to enhanced spreading and near-frictionless recoil over the intervening vapour layer, providing opportunities for external field stimulus aided modulation and control of impact outcomes, and the resulting boiling-LFE behaviour. Magnetic field modulated LFE onset, dynamics and boiling transport of stable aqueous nano Fe2O3 based ferrofluid droplets was studied using high speed imaging. The interplay between magnetic, inertia, and viscocapillary forces on droplet spreading, magneto LFE-driven rebound conditions, residence time, and post-impact regimes was analysed using dimensionless parameters maximum spread factor, Weber number, and magnetic Bond number. We report a purely new phenomenon, namely magneto Leidenfrost effect MLFE, wherein magnetic field induces LFE aided onset of droplet rebound at substrate temperatures Ts below the zero-field dynamic Leidenfrost temperature LFT. The critical for the onset of MLFE decreases with increasing . Increasing the nanoparticle concentration permits the onset even at considerably lower . At elevated Ts , the residence time is noted as dependent. At much higher Ts, increasing promotes formation of radial filamentous structures, leading to complete droplet fragmentation. We also propose a theoretical framework that explains magnetic field driven spreading enhancement and rebound, and predicts of MLFE droplets in agreement with experiments. Our findings provide valuable insights into the novel realm of field dictated LFE, and hold significant implications towards the design of frictionless, rapid colloid droplet transport systems, and targeted droplet manipulation or activation for advanced thermal management.