This paper addresses the energy dispatch of a virtual power plant comprising renewable generation, energy storage, and thermal units under uncertainty in renewable output, energy prices, and energy demand. The nonlinear dynamics and multiple sources of uncertainty render traditional stochastic model predictive control (MPC) computationally intractable as the dispatch horizon, scenario set, and asset portfolio expand. To overcome this limitation, we propose a novel controller that seamlessly integrates MPC with time series aggregation and distributed optimization, simultaneously reducing the temporal, asset, and scenario dimensions of the problem. The resulting controller provides a rigorous performance guarantee through theoretically validated bounds on its approximation error, while leveraging dual information from previous MPC iterations to adaptively optimize the temporal aggregation. Numerical results show that the proposed controller reduces runtime by over 50% relative to traditional stochastic MPC and, crucially, restores tractability where the full-scale dispatch model proves intractable.

A non-supersymmetric inverse seesaw model of neutrino mass based on the $A^{\prime}_5$ modular symmetry is presented. This framework provides a combined explanation for neutrino masses, mixing, and the cosmic baryon asymmetry through leptogenesis. Three concrete realisations are constructed, and their phenomenological predictions are analysed. The results are not only compatible with the measured neutrino oscillation parameters within the current experimental 3$σ$ ranges, but also provide predictions for the neutrino mass ordering, Dirac and Majorana CP-violating phases, and the effective Majorana mass in neutrinoless double beta decay. The model further realises TeV-scale leptogenesis consistent with the observed baryon asymmetry, rendering the scenario testable in both low-energy neutrino experiments and high-energy collider searches.

The production of very-high-energy (VHE, $E_γ \gtrsim 100$ GeV) gamma rays resulting from the scattering of high-energy cosmic-ray protons off axion-like particles (ALPs) populating the dark matter halo of the Milky Way is investigated. By employing the latest instrument response functions for current and future facilities, we demonstrate that ground-based VHE gamma-ray observatories, such as H.E.S.S., CTAO, and SWGO, provide a promising and complementary avenue to probe the yet uncharted ALP-proton coupling $g_{ap}$. Our results show that these experiments can reach sensitivity to couplings above $10^{-2}$ in the $1 - 10^{8}$ eV ALP mass range, a region that remains largely unexplored by supernova and neutron star cooling observations. Interestingly, we demonstrate that this search channel is capable of probing QCD axion dark matter models, assuming two benchmark models for it: the Kim-Shifman-Vainshtein-Zakharov (KSVZ) Dine-Fischler-Srednicki-Zhitnitsky (DFSZ) models, specifically within the MeV mass range. These findings highlight the potential of VHE gamma-ray astronomy to provide unique constraints on the interaction between ALPs and the baryonic sector.

We analyze the incompressible Navier-Stokes equations on a class of non-compact Riemannian manifolds within the framework of Morrey spaces. Assuming bounded geometry together with negative Ricci and sectional curvature (e.g., hyperbolic spaces), we establish dispersive and smoothing estimates for the heat semigroups associated with the Beltrami, Bochner and Hodge Laplacians in Morrey spaces, as well as for the Riesz transform. In particular, the presence of negative curvature yields improved large-time decay compared to the Euclidean setting. These estimates are of independent interest and enable us to construct solutions in time-weighted spaces of Kato type, leading to local-in-time well-posedness on a broad class of non-compact manifolds and global one in the case of Einstein manifolds. In the latter setting, we assume a smallness condition on the initial data in Morrey norms, which are weaker than $L^{p}$-norms and thus allow for certain classes of large $L^{p}$-data. We also discuss extensions to Ricci-flat manifolds. Our results introduce a new class of non-decaying and rough initial data for the Navier-Stokes equations on manifolds, extending previous works in Lebesgue and Sobolev spaces.

Civilization maintains an elaborate infrastructure devoted to the maintenance of synchronized time. Governments mandate daylight saving time. Standards bodies insert leap seconds into Coordinated Universal Time. Engineers debate leap milliseconds and leap nanoseconds. The Global Positioning System applies relativistic corrections at the nanosecond level. All of these adjustments attempt to preserve an assumption: that a single global time exists and that clocks can be made to agree upon it. This paper argues that this assumption constitutes a category mistake in the sense of Ryle (1949). We show that special and general relativity prohibit absolute simultaneity, that the one-way speed of light is conventionally defined rather than measured, and that recent experiments on indefinite causal order demonstrate nature admits correlations with no well-defined temporal sequence. We trace the consequences of this category mistake through distributed computing, where it manifests as the Forward-In-Time-Only (FITO) assumption that underlies Lamport's logical clocks (1978), the impossibility results of Fischer-Lynch-Paterson (1985), and the CAP theorem (2000). From this perspective, daylight saving time and leap seconds are not corrections to time but corrections to conventions -- they sharpen the guillotine of synchronization in preparation for executing something that does not exist.

Io's internal heat is generated by Jupiter-driven tidal dissipation and Laplace resonance. This energy partially melts the mantle, but the melt fraction, depth, and spatial distribution of dissipation remain poorly constrained. Tidal deformation is linked to the mantle's physical state via a parametric approach accounting for melting onset depth and latent heat of fusion. Io is modeled as a three-layer body comprising a fluid core, a viscoelastic mantle, and an elastic lithosphere. The degree-2 potential Love number k2 is computed by solving spheroidal oscillation equations with an adapted California Planetary Geophysics Code (CPGC). Mantle properties (viscosity, shear modulus, Andrade parameter beta) are iteratively updated based on the local melt fraction, and mantle compressibility is explicitly incorporated. Results show a self-consistent shallow-mantle enhancement of tidal heating. Melt presence decreases effective viscosity and increases anelasticity, amplifying upper-mantle dissipation. Reproducing the observed real part of k2 in 1D models requires melt fractions below the rheologically critical threshold. Compressibility yields higher values for the real part of k2, reinforcing this upper bound. Mass flux analysis confirms melt percolation capacity exceeds thermodynamic production, implying efficient drainage. The reference Andrade parameter beta strongly influences the imaginary Love numbers (k2, h2, l2) and the predicted libration amplitude. These constraints support a heterogeneous mantle characterized by a magmatic sponge structure rather than a global magma ocean, establishing a quantitative framework linking Io's interior, partial melting, and tidal dissipation to Juno observations.

We introduce and characterize different models for an active quantum particle where activity arises from engineered dissipation-- specifically, from a suitably coupled nonequilibrium environment. These include a model of a particle moving on a lattice with coherent and dissipative hopping, as well as quantum generalizations of well-studied models of active behavior, such as the active Ornstein-Uhlenbeck process, run-and-tumble dynamics, and the active Brownian particle. Despite the different microscopic mechanisms at play, we show that all these models display key features of active motion. Notably, we observe a crossover from diffusive to active-diffusive behavior at long times, leading to an effective Péclet number, as well as a strong sensitivity to boundary conditions which, in our open quantum system context, arises from the Liouville skin effect. We discuss the role of quantum fluctuations and experimental realizations with superconducting circuits or cold gases, closing with perspectives for many-body effects in quantum active matter.

Generative AI (GenAI) is increasingly being integrated into the online ecosystem, including online health communities (OHCs), where people with diverse health conditions exchange social support. For example, in OHCs, support providers are beginning to share content generated, directly or indirectly, by popular GenAI-based tools. OHCs are governed by norms that define appropriate behavior when providing support. Ways in which AI-generated support interacts with these norms remain underexplored. Inappropriate conformance or outright violation can erode seekers' trust, distort decision-making, and threaten community sustenance. In this work, we examine whether (and how) AI-generated support conforms to norms, using popular opioid-use recovery subreddits as our testbed. First, we provide an inventory of norms regulating text-based support provision in OHCs. Next, using human-validated LLM judges, we assess the prevalence of AI's conformity to these norms. Finally, through an expert review, we identify risks to seekers (and OHCs) resulting from norm (non)conformity. Our analysis revealed that, while AI-generated support conforms to norms, such conformity may be inappropriate or insufficient, for example, by over- or under-validating seekers in distress. Moreover, we observed instances of outright norm violation. This work provides insights that can help moderators and OHC designers adapt existing and develop new norms to regulate AI integration, protecting both seekers and communities they rely on.

Models of interacting quantum spins are used in many areas of physics ranging from the study of magnetism and strongly correlated materials to quantum sensing. In this work, we study coherent many-body dynamics of interacting spin models realized using polar molecules trapped in rearrangeable optical tweezer arrays. Specifically, we encode quantum spins in long-lived rotational states and use the electric dipolar interaction between molecules, together with Floquet Hamiltonian engineering, to realize $1/r^3$ XXZ and XYZ models. We microscopically probe several types of coherent dynamics in these models, including quantum walks of single spin excitations, the emergence of magnon bound states, and coherent creation and annihilation of magnon pairs. Our results establish molecular tweezer arrays as a new quantum simulation platform for interacting quantum spin models.

Incorporating sample efficiency, by requiring the number of states consumed by broadcasting does not exceed that of a naive prepare-and-distribute strategy, gives rise to the no practical quantum broadcasting theorem. To navigate this limitation, we introduce approximate and probabilistic virtual broadcasting and derive analytic expressions for their optimal sample complexity overheads. Allowing deviations at the receivers restores sample efficiency even in the 1-to-2 approximate setting, whereas probabilistic protocols obey a stronger no-go theorem that excludes all sample efficient 1-to-2 implementations for arbitrary dimension and success probability. Rather counterintuitive, this obstruction does not persist at larger receiver numbers: for qubit systems, practical 1-to-6 virtual broadcasting becomes attainable. These results elevate sample complexity from a technical constraint to a defining operational principle, opening an unexplored route to the efficient distribution of quantum information.

We investigate the impact of phenomenological prescriptions in the Collins-Soper-Sterman (CSS) approach for global extractions of Transverse Momentum Distributions (TMDs). We show that fits to low-energy Drell-Yan data with different choices of $b_*$ prescription yield equally good agreement with data and similar TMDs at small partonic transverse momentum. In contrast, sizable differences emerge at intermediate transverse momentum region, significantly affecting the predictions for high-energy Drell-Yan processes. Our results demonstrate that the $b_*$ prescription represents an intrinsic source of theoretical uncertainty in the CSS approach, introducing systematic effects that influence TMD extractions and their interpretation. At the same time, our analysis emphasizes the interplay between data at different energy scales in assessing the effect of phenomenological prescriptions in TMD fits adopting the CSS framework.

Ethereum Inscriptions (Ethscriptions) repurpose Ethereum calldata into a persistent inscription channel by embedding \texttt{data:}~URI payloads. These transactions typically target externally owned accounts, allowing the payload to bypass EVM execution while remaining permanently replicated across full nodes. Although calldata was originally designed for compact smart-contract parameters, this repurposing enables structured data embedding with long-term storage consequences. We present the first large-scale empirical study of Ethscriptions, treating them as a distinct \emph{calldata-resident workload} rather than merely a subset of general calldata usage. Our analysis focuses on the \textit{Ethscription} operational subset, which consists of payloads that decode to JSON and conform to a token-operation grammar (e.g., \texttt{p}, \texttt{op}, \texttt{tick}, \texttt{amt}). From $6.27$ million Ethscription candidates (\Uone), we extract $4.75$ million Ethscription operations (\Utwo, $75.8\%$ of \Uone). This result shows that structured token-like activity dominates the ecosystem. Our measurements further reveal (i) a complete workload lifecycle compressed into nine months (bootstrap, expansion, saturation), (ii) proliferation of $30$+ competing protocols without convergence toward a dominant standard, (iii) a lifecycle funnel exhibiting $201\times$ deploy-to-mint amplification and a $57.6{:}1$ mint-to-transfer collapse indicative of speculative minting, (iv) extreme participation inequality (Gini~$0.86$), and (v) a measurable permanent data footprint imposed on the Ethereum network.

Chemistry and materials science are widely regarded as potential killer application fields for quantum hardware. While the dream of unlocking unprecedented simulation capabilities remains compelling, quantum algorithm development must adapt to the evolving constraints of the emerging quantum hardware in order to accomplish any advantage for the computational chemistry practice. At the same time, the continuous advancement of classical wavefunction-theory methods narrows the window for a broad quantum advantage. Here, we explore potential benefits of quantum computation from the broader perspective of utility-scale applications. We argue that quantum algorithms need not only enable accurate calculations for a few challenging, that is strongly correlated, molecular structures, that might be hard to describe with traditional methods. Instead, they must also support the practical integration of quantum-accelerated computations into high-throughput pipelines for routine calculations on arbitrary molecules, ultimately delivering a tangible value to society.

The evaluation of elastodynamic Green's functions across numerous source-receiver locations, frequencies, and material properties, particularly in the context of parametric studies or boundary element computations, is computationally demanding and memory intensive. This paper presents a reduced order modeling strategy based on the Greedy Tucker Approximation (GTA), which incrementally constructs a low-rank representation of the Green's tensor through rank-one enrichments obtained via a Proper Generalized Decomposition (PGD)-type alternating least squares procedure. A Petrov-Galerkin formulation is employed to improve convergence and approximation accuracy. The resulting multi-dimensional tensor, expressed in terms of one-dimensional basis functions and a compact core, achieves substantial reductions in memory requirements. The methodology is demonstrated for two cases: a soil layer on rigid bedrock and a layered halfspace. Different separable dimensions are considered to capture various combinations of source and receiver configurations, frequencies, and material parameters. Results are validated against those obtained with the direct stiffness method and computation times and memory requirements are compared.

This paper outlines a conservative transport scheme for scalar tracers within a compatible finite element model for geophysical fluid equations. Instead of using the advective transport equation for a mixing ratio, a conservative transport equation is solved for the tracer density of the mixing ratio multiplied by the dry density. This ensures mass conservation in the continuous equations, which can be preserved in the discrete equations with a discontinuous Galerkin transport scheme. Our method is designed to work for two placements of the mixing ratio in a Charney-Phillips vertical staggering: either co-located with the dry density or vertically staggered from it. The new scheme is designed to conserve the tracer density and ensure consistency by maintaining a constant mixing ratio. Additionally, a mass-conserving limiter is developed to ensure non-negativity in the co-located configuration. Tests with terminator toy chemistry and a moist rising bubble show the use of the new transport scheme with physics terms and its ability to accurately model mass conservation of moisture species in a dynamical core setup.

This paper presents uniform-in-time finite-sample bounds for regularized linear regression with vector-valued outputs and conditionally zero-mean subgaussian noise. By revisiting classical self-normalized martingale arguments, we obtain bounds that apply directly to multi-output regression, unlike most of the prior work. Compared to the state of the art, the new results are more general and yield tighter bounds, even for scalar-valued outputs. The mild assumptions we use allow for unknown dependencies between regressors and past noise terms, typically induced by system dynamics or feedback mechanisms. Therefore, these novel finite-sample bounds can be applied to many affine-in-parameter system identification problems, including the identification of a linear time-invariant system from full-state measurements. These new results may lead to significant improvements in stochastic learning-based controllers for safety-critical applications.

In this study, we examine the role of a scalar field in the evolution of the Universe within the framework of a Bianchi type-I cosmological model with Lyra's geometry. Previous research has explored the nonlinear spinor field in various anisotropic and isotropic cosmological models. In our current study, we and dynamical restrictions for Lyra parameters and violation of stress-energy tensor conservation within Lyra geometry. We shown that in considering cases behavior of Lyra's parameter corresponds to relative in?uence in early universe and absence of Lyra geometry in present universe.

We establish upper bounds for the weak and strong error resulting from a perturbation of the noise driving the stochastic Burgers equation, where we assume the noise to be additive and of trace class and the initial value to be sufficiently regular. More specifically, replacing the covariance operator of the driving noise $Q_1 \in \mathcal{L}_1(L^2)$ in the Burgers equation by a covariance operator $Q_2 \in \mathcal{L}_1(L^2)$ results in a weak error of $\mathcal{O}\big(\| (-A)^{-1^{-} } (Q_1-Q_2) \|_{\mathcal{L}_1(L^2)}\big)$ and a strong error of $\mathcal{O}\big(\big\| (-A)^{-1/2^{-}}\big|Q_1^{1/2} -Q_2^{1/2}\big| \big\|_{\mathcal{L}_2(L^2)}\big)$. Here $\|\cdot \|_{\mathcal{L}_1}$ is the trace class norm, $\|\cdot \|_{\mathcal{L}_2}$ is the Hilbert-Schmidt norm, and $A$ is the one-dimensional Dirichlet Laplacian that represents the leading term in the Burgers equation. In particular, our results provide upper bounds for the weak and strong error arising when approximating the trace class noise by finite-dimensional noise; the rates we obtain reflect the general philosophy that the weak convergence rate should be twice the strong rate.

The $R$-ratio is a phenomenological observable of great relevance, both in itself and in applications such as the dispersive approach to the muon anomalous magnetic moment. It can be investigated from first-principles with controlled statistical and systematic errors in lattice QCD by introducing an arbitrary smearing kernel and employing spectral reconstruction techniques, such as the well-known Hansen-Lupo-Tantalo method. Improving upon a first study published in 2023, we show preliminary results using the correlation functions produced by ETMC in $N_f = 2+1+1$ lattice simulations at four lattice spacings, different volumes and with higher statistics w.r.t. our previous study. The new correlators, thanks to the implementation of the Low Mode Average technique, allow the determination of the $R$-ratio smeared with Gaussian kernels of widths down to $σ\sim 200$ with phenomenologically relevant precision.

The main purpose of this note is to provide an elementary discussion of some simple triangles of integer numbers in particular through their connections with representation theory of $sl_2$. The triangles under consideration are the Catalan triangle and the Motzkin triangle together with their generalisations that we introduce here. We advocate the point of view that these triangles are given by the well-known and classical Pascal rule starting from a well-chosen initial condition. We give an elementary derivation of the fact that the numbers in these triangles are multiplicities appearing in tensor products of $sl_2$-representations and that they are simply expressed as a difference of generalised binomial coefficients. We also take the opportunity to discuss the ``sum of squares'' phenomenon that happens in these triangles through the lense of representation theory.

We present a computationally efficient model that describes the interaction of intense, ultrashort infrared laser pulses with transparent materials in the strong ionization regime. The model is augmented with a detailed self-consistent description of the local response due to ionization and collisional plasma dynamics. It incorporates the direct solution of Maxwell's equations without approximations and rigorous boundary conditions for the input pulse, allowing us to study the ultrafast formation of over-critical nanoscaled plasmas in dielectric materials under the influence of intense tightly focused laser pulses. We perform a scan of the parameter space, find unexpected optima regimes for different experientially relevant parameters, and explain these maxima based on spatiotemporal dynamics.

The short quantum link regime, where the photon travel time $τ$ is comparable to the emitter lifetime $1/γ$, is experimentally relevant but theoretically underexplored: existing few-mode descriptions lose validity as retardation and multimode effects become significant. Using a Delay Differential Equation (DDE) framework that admits exact analytical solutions from the single-mode cavity limit to the multimode waveguide continuum, we show that emitters coupled to a short link spontaneously lock into self-synchronized Rabi oscillations driven by coherent photon echoes, breaking the link's discrete time-displacement symmetry. The resulting spectral structure -- persistent quasi-dark states and vacuum Rabi splitting, including in the superstrong coupling regime -- enables efficient quantum state transfer (QST): benchmarking three protocols across the full $γτ$ parameter space, we find that STIRAP exploits the quasi-dark-state structure to achieve a quadratic infidelity floor $\mathcal{O}((γτ)^2)$, outperforming both SWAP (linear error $\mathcal{O}(γτ)$) and wavepacket engineering for $γτ\lesssim 1.44$, even in regimes where retardation cannot be neglected. These results establish photon-echo synchronization as an engineering resource for quantum state transfer, with DDE modeling providing the exact analytical predictions needed to design and optimize short-link experiments on current circuit-QED hardware.

Non-Gaussian statistics are a challenge for data assimilation. Linear methods oversimplify the problem, yet fully nonlinear methods are often too expensive to use in practice. The best solution usually lies between these extremes. Triangular measure transport offers a flexible framework for nonlinear data assimilation. Its success, however, depends on how the map is parametrized. Too much flexibility leads to overfitting; too little misses important structure. To address this balance, we develop an adaptation algorithm that selects a parsimonious parametrization automatically. Our method uses P-spline basis functions and an information criterion as a continuous measure of model complexity. This formulation enables gradient descent and allows efficient, fine-scale adaptation in high-dimensional settings. The resulting algorithm requires no hyperparameter tuning. It adjusts the transport map to the appropriate level of complexity based on the system statistics and ensemble size. We demonstrate its performance in nonlinear, non-Gaussian problems, including a high-dimensional distributed groundwater model.

Transformers have revolutionized AI in natural language processing and computer vision, but their large computation and memory demands pose major challenges for hardware acceleration. In practice, end-to-end throughput is often limited by paged data movement and interconnect bandwidth rather than raw MAC count. This work proposes a unified system-accelerator co-design approach for transformer inference that jointly optimizes a matrix accelerator and its system integration through paged streaming dataflows and explicit overlap of compute and transfer. On the hardware side, we introduce MatrixFlow, a loosely coupled 16x16 systolic-array accelerator with a page-aligned block matrix multiplication method using 4 KB tiles, a small on-chip buffer of about 20 KB, and a pipelined schedule of DMA, compute, and DMA-out to utilize interconnect bandwidth efficiently. On the system side, we develop Gem5-AcceSys, an extension of the gem5 full-system simulator that explores standard interconnects such as PCIe and configurable memory hierarchies including Direct Memory, Direct Cache, and Device Memory modes with SMMU/TLB effects. We evaluate the co-design using gem5 simulations on representative transformer models including BERT and ViT across multiple data types and system setups. Results show up to 22x end-to-end speedup over a CPU-only baseline and 5x to 8x gains over state-of-the-art loosely and tightly coupled accelerators. We further show that a standard PCIe-based host-memory design can achieve about 80 percent of the performance of on-device HBM. Overall, paged streaming and pipeline overlap, rather than large local SRAMs, are the most effective levers for efficient transformer inference under realistic system constraints.

Kernel-based multivariate statistical process control (K-MSPC) extends classical monitoring to nonlinear industrial processes. Its performance depends critically on kernel parameters such as lengthscales and variance terms. In current practice these parameters are typically selected by heuristics or deterministic optimisation, and then treated as fixed, despite being inferred from finite and noisy data. This can lead to overconfident control limits and unstable alarm behaviour when the kernel choice is uncertain. This work proposes a probabilistic K-MSPC framework that quantifies and propagates kernel parameter uncertainty to the monitoring statistics. The approach follows a two-stage workflow: (i) deterministic kernel calibration using supervised or unsupervised models, and (ii) Bayesian inference of kernel parameters via Markov chain Monte Carlo. Posterior samples are propagated through kernel Principal Component Analysis to produce probabilistic $T^2$ and squarred prediction error control charts, together with uncertainty-aware contribution plots. The framework is evaluated on the Tennessee Eastman Process benchmark. Results show that posterior-mean monitoring often improves fault detection compared to deterministic prior-mean charts for the squared exponential kernel, while credible bands remain narrow in-control and widen under faults, reflecting amplified epistemic uncertainty in abnormal regimes. The automatic relevance determination kernel reduces posterior uncertainty and yields performance close to the deterministic baseline, whereas unsupervised calibration produces wider posterior bands but still robust fault detection.

Longitudinal cluster randomized trials (L-CRTs) are increasingly used to evaluate the cost-effectiveness of healthcare interventions across multiple assessment periods, yet design methods for powering these trials remain underdeveloped. Existing methods for cost-effectiveness analyses in cluster settings are limited to simple parallel-arm cluster randomized trials with a single follow-up assessment period. These methods cannot accommodate the complex correlation structures in L-CRTs conducted over multiple periods, which require differentiation between within-period and between-period correlations for both clinical and cost outcomes, as well as between-outcome correlations. Moreover, while substantial methodological advances have been made for the design of L-CRTs with univariate outcomes, none specifically address cost-effectiveness objectives where clinical and cost outcomes must be jointly modeled. We provide a design-stage framework for powering cost-effectiveness L-CRTs across three design variants: parallel-arm, crossover, and stepped wedge designs. We derive closed-form variance expressions for the generalized least squares estimator of the average incremental net monetary benefit under a bivariate linear mixed model. We propose a standardized ceiling ratio that adjusts willingness-to-pay for relative outcome variability to inform optimal design. We then develop local optimal designs that maximize statistical power under known correlation parameters and MaxiMin designs that ensure robust performance across parameter uncertainty for all three design variants. Through a real stepped wedge trial data example, we demonstrate the sample size calculation for testing intervention cost-effectiveness under local optimal and MaxiMin designs.

Conventional multi-objective optimisation approaches (e.g., MOO-CP or MIP) fail in group decision-making by aggregating heterogeneous objectives without a valid preference foundation, producing Pareto sets instead of a unique actionable decision. As only humans define objectives, preferences constitute the legitimate basis for decision-making. Accordingly, four conditions for complex design-decision systems are established: (1) Preference-Key - all objectives, constraints, and trade-offs are evaluated within a unified preference domain using valid preference function modelling (PFM); (2) Integration - feasible system performance (object capability) and acceptable actor preferences (subject desirability) coexist within a single design-decision space; (3) Association - actors freely specify individual preferences and weights, enabling consistent aggregation towards group-optimal decision-making; and (4) Uniqueness - the solver identifies a single best-fit solution with maximum aggregated preference. The ODESYS methodology, employing the IMAP solver, enables integrated multi-objective design optimisation and multi-criteria decision-making. Its extension within the ODESYS/FIVES formulation broadens applicability while achieving elegant simplicity, explicitly operationalising affine preference aggregation and preserving equivalence with validated ODESYS 1.0 results. By mapping system behaviour into a unified preference-performance domain, ODESYS/FIVES delivers a single best-fit solution, even for highly constrained problems, guaranteeing feasible and acceptable outcomes. Two applications demonstrate transformation of multi-objective optimisation into pure group decision-making, achieving a best-fit-for-common-purpose within socio-physical reach.

Topological Bloch oscillations are a hallmark of quantum transport phenomenon in which wavepackets undergo oscillatory motion driven by the interplay between an external force and topological edge states and serve as a powerful dynamical probe for the geometric properties of topological bands. Spin-orbit coupling (SOC) has also emerged as a crucial ingredient for manipulating quantum states in materials, with the corresponding gauge fields arising from the Rashba and Dresselhaus interactions. In this work, we investigate the propagation of spinor wavepackets in a honeycomb Zeeman lattice governed by the Gross-Pitaevskii equation. By tuning the relative strengths of Rashba and Dresselhaus SOC, we engineer a non-Abelian gauge field that drives anomalous topological Bloch oscillations (ATBOs). Unlike conventional topological Bloch oscillation (TBOs), these ATBOs exhibit asymmetric motion, including a freezing effect in one half of the oscillation cycle, which can be tuned by the SOC parameters and external forces. Our findings establish SOC-based non-Abelian gauge fields as a powerful mechanism controlling topological quantum dynamics, with implications for spintronic devices and quantum data processing.

Here we present broadband radio modeling of GRB 240205B, using observations with the Australia Telescope Compact Array (ATCA) and the South African MeerKAT radio telescope. Our observations include an automatically triggered early-time ATCA observation that began approximately 13 minutes after the gamma-ray signal and continued for 12 hours, resulting in the earliest detected GRB radio afterglow to date at about 35 minutes post-burst. Following this initial detection, we conducted an extensive radio follow-up campaign for more than 5 months. Although the observations beyond one day post-burst are well described by a standard forward shock model, the observation before one day post-bust reveals an additional synchrotron component, which can be explained as the reverse shock. This component would have been missed without the automated ATCA rapid-response trigger. We find that a combined reverse and forward shock model in a stellar wind medium best describes the radio afterglow. We constrain the spectral breaks due to synchrotron self-absorption and the minimum electron energy, and we use the light-curve peaks to constrain the microphysical parameters. We put GRB 240205B in the context of the growing sample of GRBs with radio detections in the first hours after the gamma-ray trigger. Using our rapid response observation, we estimate the highest model independent constraint on a GRB minimum bulk Lorentz factor of around 100 at about 35 minutes post burst. We also discuss future prospects of detecting similar long GRBs at centimeter wavelengths, as well as potential improvements to future strategies for targeting their radio afterglows.

Traditional weak gravitational lensing shear estimators are carefully calibrated but struggle to fully capture realistic galaxy morphologies, point-spread-function (PSF) effects, blending, and noise in deep surveys, while blindly trained machine learning (ML) models can introduce significant calibration biases. Here we construct a fully D$_4$-equivariant deep neural network for galaxy shape measurement whose architecture enforces symmetry under 90$^{\circ}$ rotations and mirror transformations, and adopt the Analytical Calibration framework (AnaCal) to calibrate the model using its backpropagated gradients. For isolated galaxies in LSST-like single-band simulations, we demonstrate that our approach achieves $\sim$10% lower shape noise than the traditional moment-based Fourier Power Function Shapelets estimator in the high-noise regime, equivalent to a $\sim$20% gain in effective galaxy number density, while simultaneously achieving multiplicative biases consistent with zero across a wide range of noise levels, PSF sizes and ellipticities, and magnitude selection cuts, with all measurements satisfying $|m| {<} 10^{-3}$ (i.e., within the 0.2% LSST requirement) and most at the ${\sim}10^{-4}$ level. We demonstrate this framework on isolated single-band galaxy images with Gaussian noise and known PSF, establishing a rigorous, physics-informed foundation for future extensions of ML-based shear estimation to blended sources and multi-band observations in Stage-IV surveys. All codes and data products will be made publicly available upon acceptance.