We present an automatic method based on machine-learning convolutional neural network (CNN) architecture to detect Lyman alpha emitters (LAE) hidden in the Data Release 1 spectroscopic dataset of the Dark Energy Spectroscopic Instrument (DESI). Those LAEs mostly have incorrect redshift estimations because the current DESI pipeline is not designed to detect and measure the redshifts of galaxies at $z>2$. To uncover those sources, we first visually inspect thousands of DESI spectra and construct a sample, consisting of both LAEs and non-LAEs, for training and testing the CNN-based model to (1) detect LAEs in DESI spectra and (2) determine their Ly$α$ redshifts. The final model yields $95.2\%$ purity and $95.9\%$ completeness for detecting LAEs. We apply this model to approximately $2\times10^{6}$ spectra of sources targeted as emission-line galaxies and detect 19,685 LAEs from $z\sim2$ to $3.5$ within 12 minutes with a single GPU, illustrating the high efficiency of this model for identifying LAEs. The detected LAEs are mostly at the bright end of the luminosity function with Ly$α$ luminosity $L_{\rm Lyα} \gtrsim 10^{43}$ erg/s. The high signal-to-noise composite spectrum of the detected LAEs further shows various spectral features, including P-Cygni profiles of metal lines and MgII emission lines, possible indicators of Lyman continuum escape fraction, revealing the rich astrophysical information in this LAE sample. Finally, this sample can be used to train and validate the pipelines for redshift determination of LAEs for the preparation of the DESI-II survey.

Measurement-based quantum computing uses measurement patterns on predefined quantum resource states to execute quantum logic. Quantum simulation offers an important use case on near-term devices. However, pattern optimization depends on the multivariable interplay between hardware and software constraints and is therefore use-dependent and highly non-trivial. Optimization of large-scale patterns under realistic assumptions remains a barrier. We announce the release of the quantum measurement pattern library QPatLib, a dataset that, in v1.0, presents patterns for use in measurement-based quantum simulation. We present the workflow for generating patterns that execute Pauli-string unitaries needed for many quantum algorithms. We provide benchmark patterns for measurement-based quantum unitary evolution. The measurement patterns are defined with different conventions for commuting Pauli-string subsets to allow scaling of pattern size and complexity. The purpose of the library is to (i) serve as a standardized testbed for pattern-optimization protocols for measurement-based quantum simulation routines, (ii) offer a suite of patterns for direct use on hardware, (iii) provide data to empirically justify pattern design principles, and (iv) provide a flexible resource for future storage and use of measurement-based patterns beyond quantum simulation.

Cosmic bulk flow--the volume-averaged peculiar velocity of matter--serves as a fundamental test of the Cosmological Principle when probed on gigaparsec (Gpc) scales. Historically, however, measurements of cosmic bulk flow have been limited to $R\lesssim 100\ h^{-1}{\rm Mpc}$. We present an application of kinetic Sunyaev-Zel'dovich (kSZ) velocity reconstruction to constrain the bulk flow on cosmological scales, over a volume of effective radius $R\sim2000\ h^{-1} {\rm Mpc}$. We use the WISE$\times$SuperCOSMOS and unWISE galaxy catalogs, combined with CMB temperature maps from Planck to reconstruct large-scale velocities in six tomographic bins spanning $0.1\lesssim z \lesssim 1.5$. We place some of the tightest upper limits to date on bulk velocity at $200 \lesssim R\,[h^{-1}{\rm Mpc}]\lesssim 2000$, finding results fully consistent with the $Λ$CDM bulk flow expectation. Our unWISE constraints are in strong tension with the CatWISE quasar number-count dipole measurement if that dipole is due to a coherent bulk flow $\sim 370\ {\rm km\,s^{-1}}$ at $R\sim1000\ h^{-1}{\rm Mpc}$. We also derive constraints on the matter power spectrum at low-$k$ ($k\lesssim10^{-3}\, {\rm Mpc}^{-1}$) with low-$z$ ($z\sim 1$) galaxy samples. Alongside these cosmological constraints, we introduce a novel approach to map the optical depth bias--an inherent astrophysical degeneracy in kSZ velocity reconstruction--across different data combinations. Our work bridges the theoretical gap between bulk flow and kSZ-reconstructed velocities, and expands the horizon of bulk velocity measurements out to Gpc scales.

Studies of the properties of the Quark-Gluon Plasma in high-energy heavy-ion collisions commonly facilitate proton-proton (pp) collisions at the same center-of-mass energy per nucleon pair as a reference measurement. In this paper, a deep neural network-based approach for interpolating and extrapolating pp reference transverse-momentum spectra to unmeasured energies is presented. The model is trained with ALICE data from LHC Runs 1 and 2 and provides predictions for center-of-mass energies relevant to LHC Run 3 and beyond.

Polarizable particle systems, including charged colloids, polarizable ions, biomolecular assemblies, and soft nanomaterials, can exhibit contact electrostatic interactions that depart strongly from Coulomb behavior when dielectric mismatch and geometric singularities amplify polarization effects. Here we use charged dielectric spheres as a model system and show that these polarization contributions can be canceled by jointly tuning size, charge, and dielectric asymmetries. By extending a recently developed image-charge formula to contacting dielectric spheres, we derive analytical conditions under which the contact interaction reduces to the bare Coulomb form. Accurate two-sphere calculations validate the resulting contact design rules with relative errors below $3\%$. Strikingly, many-body molecular dynamics simulations reveal that systems satisfying these two-body rules self-assemble into structures that closely match their pure Coulomb references. These results establish asymmetry as a route for turning electrostatic complexity into Coulombic simplicity at contact, with implications for controlled self-assembly and materials design.

Neural network field theory (NN-FT) formulates field theory in terms of a network architecture and a density on its parameters. We derive Schwinger--Dyson equations and Ward identities in NN-FT and utilize them to study anomalies. The equations depend on a conserved parameter space current that characterizes symmetries and how they break. It is relevant even in non-local NN-FTs, but can recover local currents in the case of a local Lagrangian by an appropriate fiber-wise average. In machine learning, this formalism is applied to feedforward networks and the attention mechanism. In physics, we use this machinery to study $U(1)$ symmetry for a complex scalar, the scale anomaly in $4d$ massless $ϕ^4$ theory, the Weyl anomaly for the bosonic string (including a new computation of the critical dimension), and examples involving discrete topological data, such as winding numbers and T-duality. Since the results are obtained in network parameter space rather than the standard field space, they represent a new way to understand symmetries in quantum field theories.

Following Veltman's approach, we formulate and discuss a general cutting equation for QED in a plane-wave background. We apply the corresponding cutting rules to justify the connection between the two-loop radiative corrections to elastic electron scattering and the rate of the trident process in a constant crossed field. As a byproduct, we compare the previously published results for the trident process in a constant crossed field and present a complete analytical expression for direct and exchange contributions to its rate, which is resolved in the spin of the initial electron. Our findings establish that although total rates can be reliably extracted from higher-loop by applying the cutting rules, reconstruction of differential rates requires additional care. The cutting rules apply to any loop order and may be extended to nonperturbative regimes.

Powder diffraction is a primary structural characterization tool in materials science, yet automated phase identification remains a major bottleneck for autonomous discovery. Existing workflows rely heavily on search--match heuristics and manual Rietveld refinement, and broadly usable end-to-end automation is especially limited for neutron powder diffraction, where comparable tools are largely absent. Here we introduce RADAR-PD, a modality-aware machine learning framework for phase identification and quantification across both X-ray and neutron powder diffraction. RADAR-PD couples a mismatch-tolerant neural network operating on coarse momentum-transfer fingerprints with automated lattice nudging and physics-constrained Rietveld verification, enabling dominant-phase hypotheses to be generated from elemental constraints and secondary phases to be isolated recursively. On an experimental RRUFF PXRD benchmark, RADAR-PD outperforms DARA in recovering the reference phase. RADAR-PD further provides robust multiphase analysis on complex time-of-flight and constant-wavelength neutron datasets, addressing an important unmet need in automated neutron diffraction analysis. These results establish RADAR-PD as an auditable, instrument-agnostic framework for autonomous structural discovery.

Color centers in silicon are emerging as promising platforms for quantum technologies. Among them, the G center has attracted considerable interest owing to its bright telecom O-band single-photon emission and its optically addressable metastable electron-spin triplet state. Here we investigate the spin properties of ensembles of G centers under above-band-gap excitation. We elucidate the spin photo-dynamics giving rise to the optical detected magnetic resonance (ODMR) response of G centers. The optimal pulsed sequence for measuring the ODMR spectrum of the G defects is identified, along with the temperature and optical-power regimes maximizing the spin readout contrast. Through magneto-optical measurements, we detect a level-anticrossing of the G center electron spin states. At last, we demonstrate coherent spin control of the defects, and characterize their spin-coherence properties. Unveiling the spin degree of freedom of the G center opens new avenues for the realization of quantum memories and quantum registers based on silicon color centers.

Topography at the core-mantle boundary (CMB) couples the outer core to the mantle and likely generates observable variations in the length of day ($Δ$LOD) and the geomagnetic field, though these effects remain poorly understood. We use direct numerical simulations of rotating shell convection with finite-amplitude CMB topography to investigate dynamical effects on the outer core. A range of topographic shapes is used, including individual spherical harmonics and a model representing seismically inferred heterogeneities in the deep mantle. As predicted by prior linear theory in the rotating annulus model, a new instability arises for Rayleigh numbers below the onset of convection; we confirm its existence in a global geometry, though the predicted scalings are quantitatively modified. The shape of the geostrophic contours -- lines of constant axial height -- plays a central role: deformed contours allow buoyancy to do work on the time-averaged flow, driving increases in Reynolds and Nusselt numbers of up to $\sim$100\% relative to a spherical boundary. Previous work showed that topographic torques scale linearly with topographic amplitude and quadratically with flow speeds; we confirm this scaling and extend it with new theory that estimates the torques for global, spectrally broad topography. When extrapolated to core conditions, the predicted torques are consistent with the magnitude required to drive observed decadal and subdecadal $Δ$LOD variations.

We discuss various forms of refinements of Vogel's universality in Chern-Simons theory. While the original universality applies to arbitrary simple Lie groups, its counterpart in refined Chyrn-Simons theory is restricted to simply laced Lie groups.

It was recently proven that, in contrast to their matrix analogues, the moments of a real Gaussian tensor of size N do not in general factorize over their connected components in the asymptotic large N limit. While the original proof of this rather surprising result was not constructive, explicit examples of non-factorizing moments, which are expectation values of trace-invariants, have since then been discovered. We explore further aspects of this problem, with a focus on Haar-distributed (or Gaussian) complex random tensors, which are more directly relevant to quantum information. We start out by exhibiting an explicit example of non-factorizing trace-invariant, thereby filling a gap in the recent literature. We then turn to the opposite question: that of finding interesting families of trace-invariants that do in fact factorize at large N. We establish three main theorems in this regard. The first one provides a sufficient combinatorial bound ensuring large N factorization, that is also simple enough to be applicable to various cases of practical relevance. Our second main result shows that the expectation value of any compatible trace-invariant is dominated by certain tree-like combinatorial structures at large N, which we refer to as tree-like dominant pairings. Our third main theorem establishes that any trace-invariant admitting tree-like dominant pairings does actually factorize at large N. In this way, we are able to prove that various families of trace-invariants that have been previously studied in the literature do factorize at large N. We apply our findings to the theory of multipartite quantum entanglement: to any trace-invariant is associated a multipartite generalization of Rényi entanglement entropy, whose typical expectation value in the uniform random quantum state can be explicitly computed assuming large N factorization.

We present a detailed chemical abundance analysis of the eclipsing binary system V505 Per. High resolution spectra were analyzed using the MOOG spectrum analysis code, and we determined abundances not only for iron and lithium but also for Si, Na, Ca, Mn, and Ni, elements that have not previously been analyzed in detail for this system. Abundances were computed across 15 temperature points using model atmospheres, with stellar parameters refined by minimizing abundance trends with excitation potential. We determined effective temperatures of T_eff = 6650 +/- 50 K for the primary and T_eff = 6550 +/- 50 K for the secondary, with iron abundances of [Fe/H] = -0.10 +/- 0.06 and [Fe/H] = -0.19 +/- 0.07, respectively. Most [X/Fe] ratios are consistent with solar values, though manganese is deficient. Our analysis of the effective temperatures shows that both stars lie on the hot edge of the lithium dip, consistent with Koenigsberger et al. (2025), which may help resolve the inconsistency noted of the stars lithium abundance within the dip by Baugh et al. (2013).

We study exact-regenerating codes for entanglement-assisted distributed storage systems. Consider an $(n,k,d,α,β_{\mathsf{q}},B)$ distributed system that stores a file of $B$ classical symbols across $n$ nodes with each node storing $α$ symbols. A data collector can recover the file by accessing any $k$ nodes. When a node fails, any $d$ surviving nodes share an entangled state, and each of them transmits a quantum system of $β_{\mathsf{q}}$ qudits to a newcomer. The newcomer then performs a measurement on the received quantum systems to generate its storage. Recent work [1] showed that, under functional repair where the regenerated content may differ from that of the failed node, there exists a unique optimal regenerating point that \emph{simultaneously minimizes both storage $α$ and repair bandwidth $d β_{\mathsf{q}}$} when $d \geq 2k-2$. In this paper, we show that, under \emph{exact repair}, where the newcomer reproduces exactly the same content as the failed node, this optimal point remains achievable. Our construction builds on the classical product-matrix framework and the Calderbank-Shor-Steane (CSS)-based stabilizer formalism.

The Galactic Centre gamma-ray excess (GCE) - an anomalous ~ 2-5 GeV Fermi-LAT signal around SgrA$^{\star}$ - has remained without a consensus interpretation for more than fifteen years. Dark-matter annihilation and unresolved millisecond-pulsar populations remain the leading candidates, yet neither incorporates the past activity of SgrA$^{\star}$ recorded by the Fermi and eROSITA bubbles (FEB). We propose a unified scenario in which both the GCE and the FEB are fossil imprints of a single past episode of SgrA$^{\star}$ activity: a precessing parabolic Blandford-Znajek jet launched from a tilted, magnetically arrested disc during a ~7.5 Myr active phase ending ~ 2.6 Myr ago. The jet both inflated the kpc-scale FEB and injected hadronic cosmic rays contributing to the GCE flux. The model rests on three independently motivated inputs: the EHT-proposed ~ $35^{\circ}$ tilt of the SgrA$^{\star}$ spin axis from the Galactic rotation axis, Lense-Thirring precession of the disc through ~5 azimuthal cycles during the active phase, and a two-zone cosmic-ray transport prescription through the CMZ and bulge with standard inner-Galaxy diffusion coefficients. Internal consistency is verified by checking that the proton Larmor radius confines cosmic rays to the jet column and that the gamma-ray optical depth toward the Galactic Centre is negligible. Comparison with current GCE observations yields a spin-dependent hadronic contribution: for the EHT-favoured SgrA$^{\star}$ spin a$^{\star}$ = 0.9, we find an irreducible hadronic floor of ~ 3-14% of the observed GCE surface brightness across the inner ten degrees, highlighting a previously unexplored component relevant for comprehensive models of the GCE.

Curvature expansion for the heat kernel trace and the one-loop effective action is built for the wave operator of the theory in the quasi-thermal setup of a nonvacuum quantum state. This setup implies a non-static and non-stationary Euclidean gravitational background with periodic boundary conditions of the period $β=1/T$, where $T$ plays the role of effective global temperature to be locally rescaled by the metric gravitational potential. The results are obtained in the approximation quadratic in metric perturbations on top of flat Euclidean space and covariantized in terms of spacetime curvature. Covariantization includes a special vector field $ξ^μ(x)$ which generalizes the Killing vector of static geometries with time translation isometry to the case of a generic arbitrarily inhomogeneous metric subject to timelike periodicity condition. This vector field is obtained as a covariant metric functional to quadratic order in metric perturbations and gives rise to the local function $T/\sqrt{ξ^2(x)}$, $ξ^2(x)=g_{μν}(x)ξ^μ(x)ξ^ν(x)$, reducing to Tolman temperature $T/\sqrt{g_{00}(x)}$ on stationary manifolds with Killing symmetry. High ``temperature'' asymptotic behavior of the nonlocal formfactors -- operator coefficients of the curvature tensor structures in the heat kernel and effective action -- are obtained and possible cosmological applications of these results are discussed.

Axions and axion-like particles are among the most promising candidates for dark matter and for manifestations of new physics beyond the Standard Model. In the present work, the contribution of axion exchange to the energy of lithium-like ions is investigated within the framework of relativistic bound-state quantum electrodynamics. A formalism for the interelectronic interaction mediated by axion exchange is developed in the Furry picture with finite nuclear size taken into account. Energy shifts are calculated for a wide range of nuclear charge numbers \(Z\) and axion masses. The magnitude of the axion-induced contribution is shown to increase with increasing \(Z\) for all states considered. Based on the analysis of lithium-like bismuth, constraints on the axion-electron interaction parameters are obtained in the high-mass region. The results indicate that precision spectroscopy of highly charged ions is a promising tool for searches for new physics associated with the exchange of pseudoscalar bosons.

Basilisk is an open-source astrodynamics simulation framework widely used for spacecraft guidance, navigation, and control (GN&C) research and development. Despite its flexibility and computational capabilities, configuring Basilisk consistently across heterogeneous development environments presents practical challenges due to dependency management, operating system compatibility, and software configuration requirements. This paper presents a Docker-based containerization workflow for Basilisk that encapsulates the complete build environment, dependencies, and simulation infrastructure within a portable container image. The workflow is demonstrated through a progression of simulation scenarios of increasing complexity, from standalone orbital dynamics scripts to BSKSim-based attitude dynamics and control simulations with Monte Carlo analysis. The BSKSim class hierarchy, dynamics model architecture, flight software implementation, and scenario execution patterns are described in detail. The presented workflow provides a self-contained implementation reference for GN&C engineers and researchers seeking reproducible and portable Basilisk simulation environments. This work expands upon a workshop presentation delivered at the 46th Rocky Mountain AAS GN&C Conference, February 2024, available at https://doi.org/10.5281/zenodo.15008785.

When multiple quantum emitters couple to a common electromagnetic environment, interference in their collective radiative dynamics gives rise to superradiance and subradiance. In regimes where coherent interactions and collective dissipation compete, the microscopic many-body dynamics and quantum correlations among the emitters that underlie superradiance and subradiance are theoretically challenging and remain experimentally elusive, even though collective emission has been observed in many physical systems. Here, we realize a superconducting qubit array coupled to a common microwave waveguide that mediates collective dissipation, with simultaneous access to coherent interactions and microscopic measurements of many-body dynamics. Engineered qubit-waveguide couplings with tunable amplitude and phase enable control of collective interference and the resulting super- and subradiant states. Leveraging site-resolved control and readout, we directly observe the microscopic decay dynamics of multi-qubit states across different excitation manifolds and track the evolution of populations and tunable quantum correlations. We reveal collective decay in regimes beyond the ideal Dicke model, where strong qubit-qubit interactions stabilize superradiance and subradiance against local dephasing and reshape decay pathways through spatially and spectrally structured many-body eigenstates. Our results establish a flexible platform for exploring collective phenomena in many-body quantum optics and driven-dissipative approaches to robust quantum information processing.

Condensed matter physics increasingly focuses on exploiting the magnetic order parameter orientation n as a tuning knob for properties of collinear magnetic materials, but a general method for constructing effective Hamiltonians with explicit n-dependence has been lacking. Here, we develop a symmetry-based framework, built on the equivariant space group, for constructing such Hamiltonians, termed equivariant magnetic Hamiltonians (EMHs). The resulting EMH lives in a higher-dimensional k-n space and exhibits unconventional symmetry actions and topological features. Using a 1D ferromagnetic chain and a 3D antiferromagnet as examples, we demonstrate that explicit n-dependence in EMHs enables the study of magnetic-dynamics-driven topological pumping, including even-integer charge pumping and a second-Chern-number-induced quantized pumping of surface anomalous Hall conductivity. Beyond model systems, we incorporate the framework into first-principles calculations to construct ab-initio EMHs that accurately capture the n-dependent band structures of real materials. The approach can also be generalized to non-collinear magnetic systems. Our work establishes a general framework for constructing EMHs and for exploring the rich physics arising from magnetic anisotropy and magnetic dynamics.

Engineered dissipation provides a powerful route to controlling and stabilizing quantum states in open systems. Superconducting circuits are particularly suited to this approach due to their tunable coupling to dissipative environments. Here we realize programmable local reservoirs for superconducting qubits through parametrically driven coupling to readout resonators, creating energy-selective incoherent pump and loss. Using coupled superconducting qubits, we autonomously stabilize entangled single-excitation states with fidelity up to 90.8%. We probe the stabilization dynamics under varying initial conditions and bath parameters, and implement robust classical shadow estimation for accurate and scalable state characterization. Finally, we numerically study a configuration where the engineered pump and loss share a common dissipative mode, leading to reservoir-mediated interference and classically correlated steady states. Our results demonstrate a scalable and hardware-efficient framework for dissipative preparation and control of correlated many-body states in superconducting circuits.

We present an update to the frequency analysis method for measuring the properties of a galactic bar. The method involves computing the fundamental frequencies of orbits in rotating, N-body-derived potential models, classifying the stars as members of bar supporting orbits, and finding the extent of the apo-centre distribution. In this work, we apply an updated classification criterion designed to isolate the so-called "Warm" inner Lindblad resonance (ILR) orbits. These orbits have been shown to contain the looped x1 orbits, which dominate the "shoulder regions" of the bar and largely contribute to the radial extent. We apply this method to existing Gaia, APOGEE, and OGLE data of more than 200,000 stars to constrain the properties of the Milky Way bar. We find that multiple bar lengths and pattern speeds are consistent with the data to within 5 percent.

Its cornerstone were studies of hadron production in deep inelastic scattering (DIS), which can be interpreted in the transverse-momentum-dependent (TMD) factorisation framework, allowing to access the distributions of polarisation and transverse momentum of quarks within the nucleon in the language of TMD PDFs, and the hadronisation in terms of TMD fragmentation functions. The data collected with a liquid hydrogen target in 2016-2017 will soon bring new information on the transverse momentum and may allow for the first time to extract the transverse polarisation of quarks within an unpolarised nucleon described by the Boer-Mulders function. The unique data collected with a transversely polarised deuteron target have already improved the knowledge of the d-quark transversity (transverse counterpart of the helicity PDF), reducing the uncertainties by a factor of 2.5 at large Bjorken x, and are yet to yield a number of interesting results.

Interplanetary dust in the inner solar system originates from multiple sources, including short-period comets and main-belt asteroids. In this work, we focus specifically on the dynamical evolution of asteroid-derived dust using N-body simulations that incorporates solar gravity, planetary perturbations, radiation pressure, Poynting-Robertson drag and solar wind forces. We quantify dust fluxes for Mars, Venus and Mercury across an important mass range, which are essential inputs for ablation process on Mars/Venus and for contributing in the impact process on Mercury. We have also derived impact velocity distributions and compared with existing literature. In addition, we examine how close-encounter velocities depend on the orbital elements linking dust energetics directly to the orbital architecture of the dust population. Our results show that the calibrated asteroidal flux agrees excellently with the scaled Grün model for Mars (0.04 orders of magnitude offset) and Venus (0.09 orders), and with the Müller (2002) model for Mercury (0.04 orders). The velocity distributions reveal a decoupling between flux and impact velocity: low-eccentricity grains dominate the flux, while high-eccentricity grains control the high-velocity tail. These findings have direct implications covering: (i) For atmosphere-less bodies like Mercury, the high-velocity tail affects impact processes and exosphere generation; (ii) For Mars and Venus, the flux-dominated low-velocity population determines meteoroid ablation rates and metal layer formation; (iii) Our calibrated fluxes provide inputs for comparison with future observations from different missions and also, for modeling impact-driven surface modification across the inner solar system.

We present a non-parametric, model-independent reconstruction of the cosmological background and perturbation dynamics in non-minimally coupled theories of gravity. Within the Effective Field Theory of dark energy framework, we reconstruct the time-dependent cosmological constant, $Λ(t)$, and the non-minimal coupling function, $Ω(t)$, from cosmological data. To ensure stability, we apply a correlated smoothness prior that restricts the reconstruction to the space of sufficiently smooth functions. Using CMB, DESI BAO, Type Ia supernovae, CMB-ISW lensing cross-correlations, and large-scale 3x2pt DES Year 3 data, we find a $2.8σ$ hint for a non-minimal coupling. For the dark energy equation of state, our results indicate a preference for the existence of crossing of the phantom divide, $w_{DE}=-1$, at $z<0.8$. The non-minimal coupling effect stabilizes dark energy perturbations, providing a viable physical interpretation of the phantom crossing scenario. Our work paves the way for model-agnostic searches for signatures of modified gravity in cosmological data.

High throughput screening was used to study of the biokinetics of F. venenatum A3/5 cultivation on alternative carbon substrates, including monosaccharides, disaccharides and mixtures relevant to food & beverage, dairy and agricultural waste streams. Expired functional drink from the beverage sector was also assessed as the primary carbon source for mycoprotein production. Growth data was analysed using modified single and multiphase Gompertz models for comparison of maximum specific growth rate and progression milestones across diverse growth regimes. Time-series substrate and byproduct data was analysed using comparative metrics, providing an explanatory basis for the different growth phenotypes observed. Substrate type strongly influenced the apparent carbon allocation strategies, with rapidly consumed sugars such as glucose and sucrose supporting high growth rates, low biomass yield and a high degree of fermentative byproduct formation. Fructose and xylose cultivations led to slower overall growth but higher biomass yield and lower byproduct formation. Galactose and lactose showed distinct dynamics that suggested co-existence of transport and metabolic induction limitations. In all dual-substrate systems, sequential utilisation was observed. However, metabolic inheritance and environmental shift effects were highlighted as potential kinetic limitations. These conditions exhibited stunted diauxic growth and low yield from secondary sugars, with glucose-dominated primary growth significantly reshaping secondary substrate efficiencies relative to their study in silo. The expired functional drink supported highly rapid growth and achieved the highest maximum specific growth rate and biomass titre of all conditions examined, alongside reduced fermentative overflow and enhanced ethanol reassimilation relative to a compositionally matched synthetic control.

Future $e^+e^-$ colliders at the Z pole place strong demands of $\frac{δL}{L}<10^{-4}$ on the integrated luminosity measurement. Small angle Bhabha scattering (SABS) remains the standard channel, while diphoton ($γγ$) events provide a complementary measurement. This contribution summarizes recent work on two dominant uncertainties. First, we investigate backgrounds to the diphoton channel and find that SABS and low-invariant-mass neutral hadrons are the most significant backgrounds. A gradient boosted decision tree (BDTG) is used to classify events by particle ID. The classification results show the existing and upgraded forward tracker and luminosity calorimeter (LumiCal) designs reject neutral hadrons but only the LumiCal upgrade can reject SABS at $\frac{δL}{L}<10^{-4}$. Second, we solve the beam deflection bias problem on an event-by-event basis using two machine learning algorithms. A BDTG and the newly written Adaptive Symbolic Memetic Regression (ASMR) are trained on beam deflection data. ASMR outperforms BDTG and provides a reduced uncertainty of $5\times10^{-6}$ for beam deflection.

A Wiener-Hopf-type integral equation for the stationary PDF of a reflected random walk is derived rigorously based on modern probability theory, and an application to battery energy storage systems (BESS), specifically the sizing of the inverter, is discussed in depth. The methodological steps include the construction of a Markov kernel, the derivation of a Fredholm integral equation of the second kind for the PDF of the BESS power, and an analytical solution of the equation based on a Neumann series. The analytical results were compared against numerical solutions obtained with the Nystrom method, as well as against the results of an algorithmic simulation using simulated input time series. The use of truncated versions of the analytic solution allows for the construction of simplified design rules for the power systems practitioner. General insights into inverter sizing criteria of storage systems for ramp-rate control of variable renewable energy (VRE) sources such as wind and solar are provided.

Tissue-scale shape changes are driven by ensembles of intracellular forces. However measuring force in these contexts remains a difficult challenge. Here we perform spectral analysis of transverse fluctuations of cell-cell junctions in \emph{Xenopus} embryonic tissue explants undergoing convergent extension. We developed an image analysis pipeline to extract fluctuation amplitude profiles $u(x,t)$ from time-lapse confocal movies and computed two-dimensional spatiotemporal power spectra. We observe power-law scaling of mean-squared fluctuation power spectra consistent with $\langle u_q^2 \rangle \sim q^{-2}$ and $\langle u_f^2 \rangle \sim f^{-2}$. The spatial scaling agrees with predictions from the Helfrich Hamiltonian, and the temporal scaling agrees with overdamped dynamics of a fluctuating membrane, both in the tension-dominated regime. Pharmacological reduction of actomyosin contractility (via low-dose blebbistatin or latrunculin B) did not significantly alter either scaling exponent. Our results provide an early empirical characterization of junction fluctuation spectra in an actively shape-changing tissue. Simple tension-dominated membrane models appear sufficient to describe transverse junction dynamics despite their active and coupled nature. This work establishes a quantitative baseline for future studies of tension-bearing tissues and motivates the development of physical models specific to multicellular systems.

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will deliver an unprecedented Type Ia supernova (SN) sample, making photometric calibration systematics a dominant source of uncertainty in dark energy constraints. We perform a comprehensive analysis of calibration systematic effects in LSST, quantifying how uncertainties in the LSST passbands propagate into biases in SN distance moduli and, consequently, the dark energy equation of state parameters. Specifically, we examine how the inferred values and uncertainties of $w_0$ and $w_a$ shift as a function of the amplitude of passband systematics. For linear passband tilts, we find that the best-fit ($w_0$,$w_a$) shifts by $\sim$0.025$σ$ and the $w_0-w_a$ contour area increases by $\sim$5% for each 1%/100nm increase in tilt, while for quadratic passband tilts, our results are less conclusive and warrant further exploration. This analysis will help inform the calibration accuracy required for LSST to achieve its goals in constraining dark energy.