Achieving atomic precision in top-down manufacturing remains a fundamental challenge nanofabrication technology. Here, the focused electron beam of a scanning transmission electron microscope is used to demonstrate atomically precise sculpting of hexagonal boron nitride (h-BN) bilayers, achieving nanoribbons as narrow as 6 Å with atomically smooth edges. The key to this precision lies in understanding how the underlying atomic structure, particularly in twisted bilayer systems, influences the milling process. High-angle annular dark-field imaging combined with multislice simulations reveals distinct intensity signatures that allow identification of different stacking arrangements within moiré patterns. Mathematical analysis of moiré lattices provides a predictive framework for determining optimal cutting directions, with cuts along armchair directions yielding superior edge quality compared to zigzag orientations. Surprisingly, a sequential milling approach, where a small electron beam subscan area is translated during the process, produces significantly better results than parallel milling of the entire target region. To understand these differences we implemented a stochastic milling model that reveals that sequential milling minimizes unwanted exposure to surrounding material through beam tail effects. These findings establish a framework for achieving atomic precision in electron beam sculpting of two-dimensional materials and provide fundamental insights applicable to the broader challenge of top-down nanofabrication.

Let $(X,T)$ be a Cantor minimal system, and let $Γ$ denote either its associated topological full group or the full group of a Bratteli diagram associated with $(X,T)$. In this paper we describe the structure of indecomposable (extreme) characters and the associated $\textrm{II}_1$-factor representations for the group $Γ$ and its commutator subgroup $Γ'$. In particular, we prove that: (1) for every nontrivial indecomposable character $χ$ of $Γ'$, there exists a finite collection (with repetitions allowed) $\{μ_i\}_{i\in I}$ of $T$-invariant ergodic measures on $X$ such that $χ(γ) = \prod_{i\in I} μ_i(Fix(γ))$, for every $γ\in Γ'$, where $Fix(γ) = \{x\in X : γx = x\}$; and (2) each indecomposable character of $Γ$ is the product of an indecomposable character of the form $\prod_{i\in I} μ_i(Fix(γ))$ and a homomorphism from $Γ$ into the unit circle. As a consequence, we show that any finite-type unitary representation of $Γ'$ that does not contain a regular subrepresentation is automatically continuous with respect to the uniform topology on $Γ'$. We also establish a general result on automatic continuity of finite-type unitary representations of infinite groups, which we use in our proofs.

Hard x-ray emission, associated with hot electron preheat, in direct-drive implosions was observed to be enhanced by a factor of $1.5\pm0.1$ by application of a $10$ T magnetic field. The applied magnetic field reaches a quasi steady-state aligned with the ablation flow prior to the onset of laser-plasma instabilities in the corona. Hot electrons that would otherwise escape the corona and lead to capsule charging in unmagnetized implosions are confined in a mirror-mode of the magnetic field in magnetized implosions. These hot electrons are shown to subsequently pitch-angle scatter from the mirror onto the capsule, thereby leading to the observed hard x-ray generation in magnetized implosions. Consequently, the energy of charged-fusion products, associated with the capsule charging, are observed to decrease when the implosion is magnetized. These results intensify the need to mitigate laser-plasma instabilities -- particularly for magnetized implosions -- to maximize fusion gain and implosion efficiency.

Plastic flow in body-centered cubic (BCC) metals and dilute/concentrated alloys is governed by the motion of <111> screw dislocations, whose glide is often impeded by cross-kinks (jogs). While existing strengthening models typically treat depinning as defect-assisted cutting or dislocation bowing, the combined strain-rate and dislocation-line-length dependence of cross-kink stability and effective obstacle spacing remains insufficiently resolved at the atomistic scale. Here, we combine conventional molecular dynamics and strain-boost hyperdynamics to investigate screw-dislocation glide in pure Nb and Mo, dilute Nb-Mo alloys, and equiatomic NbMo at 300 K over strain rates from 10^3 to 10^7 s^-1 and dislocation line lengths from 15 to 50 nm. We first demonstrate that low-strain-rate simulations require sufficiently long dislocation lines to capture consistent cross-kink behavior and strength-determining pinning events. Using the 50~nm configurations, we show that cross-kinks form not only in concentrated alloys but also in pure BCC metals, with their stability governed by the relative rates of kink nucleation and migration on primary and cross-slip planes, which differ between Nb- and Mo-rich systems due to distinct core structures and non-Schmid responses. At high strain rates, depinning proceeds predominantly via vacancy-interstitial cluster formation. In contrast, at low strain rates and long line lengths, alternative pathways emerge, including lateral cross-kink migration, three-dimensional forward--backward cross-slip, and prismatic loop formation. The effective obstacle spacing controlling the critical resolved shear stress therefore emerges from coupled thermodynamic roughening and kinetic evolution. These findings highlight the intrinsically rate-, length-, and chemistry-dependent nature of screw-dislocation strengthening in BCC alloys.

We address a question from \cite{BKV25} regarding the finiteness of the homological $R$-isoperimetric function. Let $R$ be a subfield of the complex numbers $\mathbb{C}$ with the absolute value norm. We prove that for any group $G$ that admits a finite $(n+1)$-dimensional model for $K(G,1)$, the homological $n$-isoperimetric function of $G$ over $R$ is either linear or takes infinite values. In particular, by results of Gersten and Mineyev, in the class of groups admitting a finite $2$-dimensional classifying space, the homological $1$-dimensional isoperimetric function over $R$ only captures hyperbolicity. This follows as a particular case of a more general result proved in this note.

We introduce the notion of two-sided Ehresmann semigroupoids and show that they are in correspondence with a specific class of categories, which we call local biordered Ehresmann categories. This correspondence provides a unified generalization of the Ehresmann-Schein-Nambooripad Theorem for both inverse semigroupoids and Ehresmann semigroups. In particular, two-sided restriction semigroupoids form a distinguished subclass of two-sided Ehresmann semigroupoids, and for this case we describe the associated class of categories, extending earlier results for restriction semigroups.

We consider relative character varieties on $\mathbb{P}^1\backslash\{0,1,\infty\}$ with $G=GL(r), O(r)$, or $Sp(r)$. Using a diagrammatic method of Simpson's, we give an explicit linear upper bound $R(d)$ on the rank $r$ of an MC-minimal character variety of dimension $d>2$. An arbitrary character variety is isomorphic, via Katz's middle convolution, to one satisfying the bound. For the general linear and non-overlapping quadratic cases, the bounds we give are the sharpest possible using this method.

The spin-ladder compounds of the BaFe2X3 (X = chalcogen) family may be viewed as dimensional reductions (along stripe-like motifs) of the two-dimensional iron-based pnictide planes extensively studied since 2006. Remarkably, despite their reduced dimensionality, these materials retain the capacity for unconventional ground states, exemplified by the emergence of superconductivity in \bfse\ under applied pressure beyond 10 GPa, following a structural phase transition at 4 GPa. Here, we report a comprehensive investigation combining high-resolution single-crystal X-ray diffraction, infrared spectroscopy, and ab initio calculations, which together elucidate the true crystallographic nature of this pressure-induced superconducting phase. While X-ray diffraction alone reveals a symmetry lowering from the widely accepted orthorhombic Cmcm group to a monoclinic structure, it lacks sufficient sensitivity to resolve the precise space group. By integrating vibrational spectroscopy with density functional theory, we provide unambiguous evidence that the high-pressure phase is non-centrosymmetric, adopting the polar space group P2_1. These findings not only revise the structural assignment of \bfse\ in its superconducting state but also establish its non-centrosymmetric character (an essential ingredient for potential unconventional pairing mechanisms- thereby opening new perspectives on the interplay between lattice symmetry, dimensionality, and superconductivity in iron-based materials.

We test the Etherington cosmic distance-duality relation (CDDR), by comparing Type Ia supernova (SNIa) luminosity-distance information from the Pantheon+ compilation with an angular-diameter-distance reconstructed from localized Fast Radio Bursts (FRBs). The core of our methodology is a data-driven reconstruction from FRBs using artificial neural networks (ANNs): we infer a smooth mean extragalactic dispersion-measure relation and use its redshift derivative to recover $H(z)$ and hence $D_\mathrm{A}^{\rm FRB}(z)$ without assuming a parametric form for the expansion history. Possible deviations from CDDR are parameterized through three one-parameter models of $η(z)\equiv D_\mathrm{L}/[(1+z)^2D_\mathrm{A}]$. We implement two complementary likelihoods: (i) a direct approach using individual SNIa with the full Pantheon+ covariance, and (ii) a machine-learning approach in which we reconstruct the SN Hubble diagram on the FRB redshift grid, propagating SN and FRB uncertainties into non-diagonal covariance matrices via Monte Carlo and bootstrap realizations. Within the FRB reconstruction, we anchor the mean extragalactic dispersion measure at $z=0$, which yields a data-driven constraint on the average host/near-source contribution $\mathrm{DM}_{\rm host}=128.8\pm 34.1\,\mathrm{pc\,cm^{-3}}$ ($3σ$ of statistical confidence). We find that both likelihood implementations give consistent posteriors and no statistically significant evidence for departures from CDDR at the current precision.

We perform the first systematic study of the minute-to-hours-timescale stochastic variability observed in the X-ray luminosity of tidal disruption events (TDEs) using XMM-Newton data and Fourier analysis methods. We measure the spectral properties, power spectral densities (PSDs), fractional variability amplitudes, and energy dependence of the variability for 18 TDEs spanning 54 observations, of which 27 occur in thermal disk-dominated states and 27 show a nonthermal hard X-ray corona. Compared to pure thermal sources, we find TDEs with coronae are more X-ray variable and show steeper PSDs indicating longer correlation timescales. This state-transition behavior is qualitatively similar to X-ray binaries, which show higher fractional variability in the hard state than in the soft state. However, newborn TDE coronae show systematically flatter PSDs and softer energy spectra than their long-lived AGN counterparts. We also show that the variability amplitude of thermal TDEs increases with photon energy, consistent with variations sourced by local temperature fluctuations and exponentially enhanced in the Wien tail. Our work demonstrates that combining spectral and timing properties of X-ray TDEs can probe the microphysics of newly formed accretion flows around supermassive black holes, and that the coronae formed in TDEs fundamentally differ from those in AGN.

In this paper, we study the {\em green bin packing} (GBP) problem where $β\ge 0$ and $G \in [0, 1]$ are two given values as part of the input. The energy consumed by a bin is $\max \{0, β(x-G) \}$ where $x$ is the total size of the items packed into the bin. The GBP aims to pack all items into a set of unit-capacity bins so that the number of bins used plus the total energy consumption is minimized. When $β= 0$ or $G = 1$, GBP is reduced to the classic bin packing (BP) problem. In the {\em constrained green bin packing} (CGBP) problem, the objective is to minimize the number of bins used to pack all items while the total energy consumption does not exceed a given upper bound $U$. We present an APTAS and a $\frac 32$-approximation algorithm for both GBP and CGBP, where the ratio $\frac 32$ matches the lower bound of BP. Keywords: Green bin packing; constrained green bin packing; approximation scheme; offline algorithms

This letter investigates properties of the second-order cone relaxation of the optimal power flow (OPF) problem, with emphasis on relaxation tightness, nodal voltage angles recovery, and alternating-current-OPF feasibility in meshed networks. The theoretical discussion is supported by numerical experiments on standard IEEE test cases. Implications for power system planning are briefly outlined.

We study the stability of the ordered phase of compressible polar flocks against the nucleation of counter-propagating droplets, using a combination of analytical theory, microscopic and hydrodynamic simulations. For discrete-symmetry flocks, such droplets are known to always grow and propagate, making the ordered phase metastable. We explain how, on the contrary, continuous symmetry can stabilize the ordered phase at small enough noise by destabilizing the leading edge of growing droplets. Flocking models with continuous symmetries thus have a lower critical dimension than their discrete-symmetry counterparts, in contrast to equilibrium physics.

Plates generally admit six deformation modes: three of which are high in strain energy, stretch the plate's midsurface and are called membrane modes; and three are low-energy, bend the midsurface without stretching it and are called bending modes. For origami tessellations, and other corrugated compliant thin shells, the modes are mixed and it is no longer clear what modes, if any, are low in energy in the sense that they are inextensional. Here, it is shown, by direct construction of closed-form solutions, that when the midsurface is a surface of translation, there exists three infinitesimally inextensional deformation modes that correspond to (1) stretching, with an effective Poisson's effect; (2) bending, with an effective synclastic or anti-clastic effect; and to (3) twisting. The provided expressions are valid irrespective of surface regularity and, in particular, properly handle any creases be them straight or curved. The results provide a powerful benchmark for the validation of numerical methods and further insight into the elastic stiffness of thin corrugated compliant shells.

The deployment of deep learning inference in production environments continues to grow, where throughput, latency, and hardware efficiency are critical. Although specialized accelerators are increasingly adopted, many inference workloads still run on CPU-only systems, particularly in legacy data centers and cost-sensitive environments. This study investigates the scalability limits of CPU-based inference for convolutional neural networks by benchmarking ResNet models across varying batch sizes on two hardware tiers: a legacy Intel Xeon E5-2403 v2 processor and a modern Intel Xeon 6 "Granite Rapids" platform. Results show that legacy CPUs quickly reach throughput saturation, with limited scaling beyond small batch sizes due to instruction-level and memory constraints. In contrast, the Granite Rapids system leverages Intel Advanced Matrix Extensions (AMX) to achieve substantially higher throughput. However, oversubscription beyond physical core limits introduces execution contention and tail-latency amplification, revealing a performance degradation regime in modern architectures. We introduce GDEV-AI, a reproducible benchmarking framework for analyzing scalability behavior and architectural saturation in CPU-based inference. By establishing a vendor-neutral baseline, this work provides empirical insight into performance bottlenecks and informs capacity planning in heterogeneous data center environments.

This paper introduces a distillation framework for an ensemble of entropy-optimal Sparse Probabilistic Approximation (eSPA) models, trained exclusively on satellite-era observational and reanalysis data to predict ENSO phase up to 24 months in advance. While eSPA ensembles yield state-of-the-art forecast skill, they are harder to interpret than individual eSPA models. We show how to compress the ensemble into a compact set of "distilled" models by aggregating the structure of only those ensemble members that make correct predictions. This process yields a single, diagnostically tractable model for each forecast lead time that preserves forecast performance while also enabling diagnostics that are impractical to implement on the full ensemble. An analysis of the regime persistence of the distilled model "superclusters", as well as cross-lead clustering consistency, shows that the discretised system accurately captures the spatiotemporal dynamics of ENSO. By considering the effective dimension of the feature importance vectors, the complexity of the input space required for correct ENSO phase prediction is shown to peak when forecasts must cross the boreal spring predictability barrier. Spatial importance maps derived from the feature importance vectors are introduced to identify where predictive information resides in each field and are shown to include known physical precursors at certain lead times. Case studies of key events are also presented, showing how fields reconstructed from distilled model centroids trace the evolution from extratropical and inter-basin precursors to the mature ENSO state. Overall, the distillation framework enables a rigorous investigation of long-range ENSO predictability that complements real-time data-driven operational forecasts.

This paper makes significant progress towards resolving a conjecture relating strong forcing axioms like $PFA$ and the derived model at a limit of Woodin cardinals $κ$. In particular, using a concept called Covering Matrices, we show that the $Θ$ of the derived model at $κ$ is strictly less than $κ^+$ under various circumstances; in particular, this shows that the conclusion holds under $PFA$ if $κ$ is a limit of Woodin cardinals of cofinality $ω$ and the derived model does not satisfy $LSA$. Assuming a form of mouse capturing, we show that the derived model satisfies $AD_{\mathbb{R}}$ under $PFA$ when $κ$ is a regular limit of Woodin cardinals. If $κ$ is an indestructibly $(κ,κ^+)$-weakly compact limit of Woodin cardinals, then the derived model outright satisfies $AD_{\mathbb{R}}$.

Ever since the introduction of internet technologies in South Korea, digital sexual violence (DSV) has been a persistent and pervasive problem. Evolving alongside digital technologies, the severity and scale of violence have grown consistently, leading to widespread public concern. In this paper, we present four eras of image-based DSV in South Korea, spanning from the early internet era of the 1990s to the deepfake scandals in the mid-2020s. Drawing from media coverage, legal documents, and academic literature, we elucidate forms and characteristics of DSV cases in each era, tracing how entrenched misogyny is reconfigured and amplified through evolving technologies, alongside shifting legislative measures. Taking a genealogical approach to read prominent cases of different eras, our analysis identifies three constitutive and interconnected dimensions of DSV: (1) the homo-social fabrication of "obscenity", wherein victims' imagery becomes collectively framed as obscene through participatory practices in male-dominant networks; (2) the increasing imperceptibility of violence, as technologies foreclose victims' ability to perceive harm; and (3) the commercialization of abuse through decentralized economic infrastructures. We suggest future directions for CSCW research, and further reflect on the value of the genealogical method in enabling non-linear understanding of DSV as dynamically evolving sociotechnical configurations of harm.

We introduce MxDiffusion, a hybrid physics- and data-driven diffusion-based framework that enables efficient and highly accurate generation of photonic structures from target optical properties. The improved accuracy is achieved through a two-stage generation strategy, in which the first diffusion model is explicitly trained with Maxwells equation-based loss to embed physical insight directly into the inverse design process, while the second model maps the physically consistent intermediate representation to the final structural geometry with significantly higher fidelity than solely data-driven approaches. The performance of MxDiffusion is validated on two representative applications: gold nanostructures patterned on a silica substrate and a highly tunable bandpass filter based on phase change material. In both cases, the proposed framework consistently outperforms a conventional data-driven diffusion model benchmark, particularly for out-of-training-distribution design targets and highly constrained resonance conditions. These results demonstrate the efficacy and superiority of MxDiffusion as a general physics-guided inverse design paradigm.

This paper develops an end-to-end odor communication model for stress signaling between plants using Green Leaf Volatiles (GLV). A damaged transmitter plant emits (Z)-3-hexenal, (Z)-3-hexenol, and (Z)-3-hexenyl acetate, which propagate through a time-varying diffusion-advection channel and undergo multiplicative loss at the receiver. The sink plant is modeled with a biochemical receiver network that converts the received GLVs into the defensive metabolite (Z)-3-hexenyl $β$-vicianoside, and an alarm decision is defined based on its concentration level. Numerical results show that (Z)-3-hexenol is the primary driver of the system and that plant perception generally operates in a non-linear region. These findings provide a framework for understanding the evolution of plant-plant communication and for developing next-generation precision farming technologies.

The existence of generalized steady states (GSSs) in nonlinear mechanical systems under moderate temporally aperiodic forcing has only been shown recently. Here we derive systematic expansions for such GSSs and construct a numerical algorithm that yields explicit and arbitrarily refinable approximations for GSSs without the need for an initial convergence period. This is to be contrasted with a direct numerical integration of the system, whose convergence is hard to assess or is even undefined for short, transient forcing. When at least the linear part of the equations of motion is known, our GSS algorithm outperforms available data-driven neural-network-based techniques for predicting forced response in structural dynamics problems. In a fully equation-driven setting, our GSS computations are shown to be faster than a direct numerical integration of forced nonlinear finite-element models of beams and shells.

Asteroid activity (e.g., thermo-mechanical breakdown, impacts, rotational shedding, tidal disruption, etc.) can inject meteoroids into near-Earth space and leave detectable signatures in orbit catalogs. We searched for such recent signatures using orbit-similarity statistics and explicit null-hypothesis testing applied to shower-removed, asteroidal video-meteor datasets. Our sample comprises 235{,}271 meteors and fireballs from four all-sky video networks (GMN, CAMS, EDMOND, and SonotaCo). For meteors we use the geocentric dissimilarity criterion $D_N$ and construct KDE-based sporadic null realizations to evaluate (i) global cumulative similarity distributions and (ii) localized $D_N$-conditioned ($D_N<0.015$) pair-excess maps in the $(U,λ_\odot)$ plane; we additionally apply DBSCAN ($ε=0.03$, $\mathrm{min\_samples}=2$) to isolate the coherent, statistically significant structures. We find no survey-consistent, stream-like signature in the Earth-like, low-inclination region expected for a distinct \emph{recent} tidal-disruption family; instead, significant-bin membership implies, under our adopted detection thresholds and binning, a conservative combined upper limit of $\leq 53/235{,}271$ ($\leq 2.3\times10^{-4}$) for sporadic asteroidal meteors plausibly attributable to a detectable recent tidal-disruption-like contribution. In contrast, we confirm the detection of a new diffuse southern Virginid-region stream: GMN exhibits a local z-score of 6.32 relative to the KDE-null mean in the $U-λ_\odot$ phase space (global significance of 5.3~$σ$), with weaker supporting excess in SonotaCo and EDMOND. DBSCAN isolates $N=282$ members (243 GMN plus additional SonotaCo, CAMS, and EDMOND) on a low-perihelion, asteroidal orbit ($q=0.22\pm0.01$ au, $i=12.3^{\circ}\pm1.8^{\circ}$, $T_J=4.6\pm0.3$) consistent with near-Sun thermo-mechanical ``rock-comet'' activity.

Envelope extraction in nuclear magnetic resonance (NMR) is a fundamental step for processing the data space generated by this technique. Envelope detection accuracy improves with increasing the number of sampling points; however, we propose a novel transform that enables acceptable envelope extraction with significantly fewer sampling points, even without meeting the Nyquist rate. In this paper, we challenge the traditional scale definition and demonstrate that classic scaling lacks a physical referent in all situations. To achieve this aim, we introduce a scale based on the variations of space-invariant states, rather than the observable characteristics of matter and energy. According to this definition of the scale, we distinguished two kinds of observers: scale-variant and scale-invariant. We demonstrated that converting a scale-variant observer to a scale-invariant observer is equivalent to envelop extraction. To analyse and study the theories presented in the paper, we have designed and implemented an Earth-field NMR setup and used real data generated by it to evaluate the performance of the proposed envelope-detection transform. We compared the output of the proposed transform with that of classic and state-of-the-art methods for parameter recovery of NMR signals.

We present a novel solution to the cosmological constant (CC) problem that requires no fine-tunings nor anthropic reasoning. In pre-geometric gravity (PGG), spacetime emerges from the spontaneous breaking of a fundamental gauge symmetry. This mechanism dynamically generates general relativity while also revealing a deep connection: the topological Gauss-Bonnet coupling of the theory scales precisely as the de Sitter entropy, an enormous number which reflects the information content of our universe. This coupling acts as a gravitational $θ$-angle parameter, forcing the CC to become quantized into discrete topological sectors. The symmetry-breaking dynamics naturally selects the sector corresponding to the observed vacuum energy. The selected vacuum state is stabilized by the extremely large potential barrier of the pre-geometric Higgs field, which effectively seals it off from quantum tunneling transitions to other topological sectors. The PGG framework thus provides a dynamical explanation for the smallness of the CC, linking gravity, topology and quantum information in a unified picture.

We prove generalizations of the first and second Ray-Knight theorems, for a large class of non-symmetric strong Markov processes. These results link the local times of the Markov process with the squares of associated Gaussian processes. This connection allows us to establish results about the exact modulus of continuity (in the spatial variable) of the local times. Our approach is different from earlier treatments which were based on associated permanental processes rather than Gaussian processes. The type of process with which we work can be described as follows. Start with a symmetric Markov process with finite lifetime; upon its death resurrect it at a place in the state space chosen at random, independent of the past. Continue in this way, resurrecting at each death, to obtain a recurrent process. The rebirthing procedure destroys the symmetry of the original process, leading to a large class of non-symmetric processes. The main results are illustrated by many examples.

We discuss new-physics searches and symmetry tests with dipole moments, emphasizing the role of polarization observables. As a primary benchmark, we consider polarized photon fusion in the $e^+ e^-$ environment of the Super Tau-Charm Facility (STCF) and study $γγ\to τ^+ τ^-$ in the nearly back-to-back region, where a transverse-momentum-dependent (TMD) description provides a convenient framework for organizing polarization effects. We show that linearly polarized photons induce characteristic azimuthal asymmetries in the $τ^+ τ^-$ kinematics, enabling polarization-based observables that enhance sensitivity to the $τ$ electromagnetic dipole form factors. Moreover, $CP$-even and $CP$-odd dipole interactions can be disentangled through distinct angular structures, offering a systematic path to probe $τ$ dipole moments with improved precision at future lepton colliders. As an illustration, we obtain an improved $2σ$ reach on the anomalous magnetic dipole moment, $-4.6 \times 10^{-3} < \mathrm{Re}(a_τ) < 7.0 \times 10^{-3}$, reaching a precision level close to the Standard Model expectation. To place these prospects in a broader context, we briefly summarize the experimental status of dipole-moment measurements across different fermionic systems and highlight their complementarity in constraining new physics. We illustrate this interplay with supersymmetric scenarios featuring $R$-parity violation, in which loop-induced dipole moments provide correlated probes of $CP$-conserving and $CP$-violating interactions. Taken together, polarized photon fusion and precision dipole measurements constitute a coherent program for testing fundamental symmetries and exploring physics beyond the Standard Model.

This paper presents a low-thrust trajectory optimization strategy to achieve a near-circular lunar orbit for a CubeSat injected into a lunar flyby trajectory. The 12U CubeSat HORYU-VI is equipped with four Hall-effect thrusters and designed as a secondary payload on NASA's Space Launch System under the Artemis program. Upon release, the spacecraft gains sufficient energy to escape the Earth-Moon system after a lunar flyby. The proposed trajectory is decomposed into three phases: (1) pre-flyby deceleration to avoid heliocentric escape, (2) lunar gravitational capture, and (3) orbit circularization to the science orbit. For each phase, an impulsive-burn solution is first computed as an initial guess, which is then refined through finite-burn optimization using Sequential Quadratic Programming (SQP). The dynamical model incorporates Earth-Moon-Sun-Jupiter gravitational interactions and a high-fidelity lunar gravity field. All trajectories are independently verified with NASA's General Mission Analysis Tool (GMAT). Results demonstrate that HORYU-VI achieves lunar capture within 200 days, establishes a stable science orbit at 280 days, and can spiral down to a near-circular 100 km orbit by 450 days, using a total Delta-V of 710 m/s, well within the capability of the electric propulsion system.

Nonreciprocal theories are used to model a broad array of non-equilibrium phenomena found in nature ranging from biological systems like networks of neurons to the behavior of overflowing water fountains. This includes systems broadly classified as active matter systems. We show that the stochastic theories which describe nonreciprocal interactions can be mapped into quantum field theories described by a supersymmetric action with a single supercharge. The theories are generically non-Hermitian. This generalizes the past work of Parisi and Sourlas on reciprocal theories, which model systems with interactions derived from potentials.

We consider a dark sector consisting of fermionic dark matter (DM) charged under a broken dark $U(1)_D$ gauge symmetry, interacting with the Standard Model through kinetic mixing. In such models, the DM annihilation cross section is typically suppressed by the small kinetic mixing and or a heavy mediator, often leading to an overabundant relic density. We show that the observed DM abundance can be achieved if the dark Higgs undergoes a strong first order phase transition after DM freeze-out. In this scenario, the relic abundance is set by thermal freeze-out in the symmetric phase and subsequently reduced by entropy injection from the phase transition, rather than by annihilation in the broken phase. We find that to reproduce the observed relic abundance, the required phase transition is generically supercooled. The resulting stochastic gravitational wave signal lies within the sensitivity of future experiments, providing a complementary probe of this framework. Moreover, a strongly supercooled phase transition can potentially account for the NANOGrav signal for DM masses below $O(10)$ GeV.

Neutrino emission from astrophysical sources has long been considered a signature of cosmic-ray acceleration. The IceCube neutrino observatory has observed a diffuse flux of TeV-PeV neutrinos, but very few confirmed sources have emerged. With the recent publication of IceCube Event Catalog (IceCat-1), IceCube has released a list of the most promising astrophysical neutrino events since May 2011. Using the archival data from the High Altitude Water Cherenkov (HAWC) $γ$-ray observatory, we perform a coincidence search for gamma rays and neutrinos using a Bayesian Block algorithm with the public IceCube alerts from IceCat-1, along with additional alerts issued later. In this work, we consider 368 alerts, up to July 8, 2025, that are within HAWC's field of view. We observe approximately a 5\% coincident detection rate, which is consistent with expectations from background. Two of these detections contain the Active Galactic Nuclei (AGN) Markarian 421 and Markarian 501. We discuss the likelihood that the neutrino/$γ$-ray coincidences are false positives and a brief overview of the results.