Software verification is now costly, taking over half the project effort while failing on modern complex systems. We hence propose a shift from verification and modeling to herding: treating testing as a model-free search task that steers systems toward target goals. This exploits the "Sparsity of Influence" -the fact that, often, large software state spaces are ruled by just a few variables, We introduce EZR (Efficient Zero-knowledge Ranker), a stochastic learner that finds these controllers directly. Across dozens of tasks, EZR achieved 90% of peak results with only 32 samples, replacing heavy solvers with light sampling.

We theoretically investigate how information flows when two particles interact with each other. Understanding the physical mechanisms of directional information flow is crucial for advancing information thermodynamics and stochastic computing. However, the fundamental connection between mechanical motion and causal information transfer remains elusive. To focus only on essential effects of physical dynamics, we examine two interacting Brownian particles confined in a one-dimensional potential. By simulating their Langevin dynamics, we quantify the causal information exchange using transfer entropy. We demonstrate that a mass asymmetry inherently breaks the symmetry of information flow, inducing a net directional transfer from the heavier to the lighter particle. Physically, the heavier particle, possessing larger inertia and higher active information storage, retains the memory of its trajectory longer against thermal fluctuations, thereby acting as a source of information. We analytically clarify that this net transfer is governed by a competition between the difference in memory capacity and the predictability of the particle trajectories. Furthermore, we reveal that the net information flow scales logarithmically with the mass ratio. These findings provide essential insights into the physical significance of transfer entropy and the nature of information flow in general physical systems.

The accelerating gradient of Nb3Sn superconducting radiofrequency (SRF) cavities is currently limited, and the underlying cause remains an open question in the field. One leading hypothesis attributes this limitation to the presence of tin-deficient regions within the Nb3Sn coating, which can suppress the superheating field. Due to the relatively large coherence length of Nb3Sn, defects near the surface may significantly interact with the RF field. However, these subsurface defects have proven difficult to characterize. This research aims to investigate the structure and distribution of subsurface Sn deficient regions to better understand their influence on cavity performance. We employ focused ion beam (FIB) tomography to analyze the subsurface microstructure of Nb3Sn thin films. This technique enables three-dimensional reconstruction of both the tin distribution and the grain structure within the film. By correlating Sn content with grain structure, we find that Sn deficient regions are more prevalent that previously thought. However, the Sn deficient regions are consistently located below the surface of the film where RF fields are strongly attenuated by supercurrent screening and are likely not a limiting factor for cavity performance.

Although two-dimensional periodic structures have functioned as the primary platform for exploring topological phenomena, recent advances have substantially expanded this research boundary to include more intricate, aperiodic structures: quasicrystals, fractals, non-Euclidean lattices, and disordered materials. A network-based perspective not only offers a unified framework for classifying these diverse platforms based on their network connectivity but also unveils unexplored regimes of topological phenomena in complex networks. Here, we implement topological heavy-tailed networks, as an example of high-degree complex networks exhibiting topological phases. By developing a tight-binding model for the Apollonian network and a deterministic algorithm to assign nontrivial gauge fields to this aperiodic geometry, we compute the magnetic-flux-dependent energy spectrum: the Apollonian butterfly. Using spectral localizers, we characterize the topological features of the Apollonian butterfly, whose sensitivity is governed by lower-degree nodes, analogous to the controllability of complex networks. Our framework bridges topological physics and network science, introducing a connectivity-driven paradigm for the control of topological waves.

We investigate the notion of partial trace ideals, recently introduced by Maitra. We first establish several properties of partial trace ideals and give affirmative answers to questions posed by Maitra. We then study the invariant defined by the partial trace ideal of the canonical module, and obtain an upper bound that recovers one direction of a result of Kobayashi. Moreover, in the case of numerical semigroup rings generated by three elements, we provide an explicit formula for this invariant.

This perspective overviews the family of two-dimensional (2D) materials, which have attracted significant attention due to their properties and potential applications, and discusses how novel 2D materials including van der Waals (vdW) and non-vdW 2D materials have been predicted so far. A few thousand 2D materials have been predicted to be exfoliable or dynamically/thermodynamically stable, whereas a few hundred 2D materials have been synthesized so far, highlighting a gap between the theoretical prediction and experiments. This perspective introduces the recent developments in predicting the synthesis of non-vdW 2D materials.

We investigate the dynamics of ferroelectric textures driven by polarization currents. We show that, ferrons, the quanta of collective polarization excitations, provide an exotic driving mechanism for domain wall (DW) dynamics, compared with their magnonic counterparts. By mapping the linear polarization dynamics of a DW onto a Schrödinger-like problem with a Pöschl-Teller potential, we show that polarization waves are fully transmitted and therefore do not exert a net force on the DW in the linear regime. However, intrinsic nonlinearities give rise to a negative radiation pressure that pulls the DW toward the source. This mechanism allows efficient DW control by optical excitation and temperature gradients with application potential in ferroelectric memory and logic devices.

We consider polynomials on the intersection of the closed positive orthant with the height-$1$ level hypersurface of certain polynomials with positive coefficients. We show that any polynomial strictly positive on such a semi-algebraic set can be represented by some polynomial with only positive coefficients. This result generalizes a result of Pólya which corresponds to the case when the semi-algebraic set is the standard simplex. Our proof uses the Archimedean Representation Theorem from real algebra.

The COVID-19 pandemic exposed young people to a prolonged and evolving societal stressor, yet it remains unclear whether symptom networks were reorganized or whether control was redistributed across a conserved modular scaffold. Here we analysed repeated cross-sectional data on 47 self-reported mental-health symptoms from 14,181 U.S. young adults aged 18-24 years across five COVID-19 phases between 2020 and 2023. For each phase, we estimated Gaussian graphical models, identified symptom communities, and characterized minimum-dominating-set-based module control. Symptom networks showed broadly conserved community organization across phases, indicating a stable mesoscale scaffold despite marked temporal variation. By contrast, intermodule control shifted from an early configuration centered on stress-related symptoms to a later, more distributed pattern spanning emotional, cognitive and social domains. Resampling analyses showed high stability for node strength and moderate stability for module-to-module control, whereas average within-module control was less robust. These findings suggest that prolonged crisis may preserve the modular architecture of youth psychopathology while redistributing control across symptom domains, and they identify intermodule control as a comparatively robust mesoscale feature for cross-phase comparison.

Radio Dynamic Zones (RDZs) are geographically defined areas specifically allocated for testing new wireless technologies. It is essential to safeguard the regular spectrum users outside the zones from the interference caused by the deployed equipment within this zone. Previous works have utilized sparse reference signal received power (RSRP) measurements collected by unmanned aerial vehicles (UAVs) to construct a dense 3D radio map through ordinary Kriging. In this work, we illustrate that matrix completion can outperform ordinary Kriging. We partitioned a 2D area of interest into small square grids where each grid corresponds to a single entry of a matrix. The matrix completion algorithm learns the global structure of the radio environment map by leveraging the low-rank property of propagation maps. Additionally, we illustrate that the simple Kriging and trans-Gaussian Kriging yield better results when the density of known measurements is lower. Earlier works of RSRP prediction involved a training dataset at a single altitude. In this work, we also show that performance can be improved by utilizing a combined dataset from multiple altitudes.

For radially excited heavy quarkonia $ V=ψ(2S) $ and $ Υ(2S) $, the nodal structure of their wave function renders the three-gluon decay $ V\to ggg $ acutely sensitive to relativistic corrections, presenting a longstanding challenge for reliable theoretical predictions. We perform a comprehensive analysis of these decays within the Bethe-Salpeter formalism, constructing analytic harmonic oscillator wave functions that explicitly incorporate the $2S$ nodal structure. Model-independent relations among polarized decay widths are derived from helicity-flip and phase-space symmetries. To obtain physically consistent results beyond $ q^2 $-order relativistic corrections, we introduce a concise phenomenological treatment that effectively incorporates partial higher-order contributions while preserving the correct low-momentum limit. Taking into account both relativistic and QCD radiative corrections, we compute $ Γ(V\to ggg) $, $ Γ(V\to e^+e^-) $, and their ratio $ R_V = Γ(V\to ggg)/Γ(V\to e^+e^-) $, which allows a clean extraction of the harmonic oscillator parameter $ β_V $, with the extracted values lying at the lower end of the ranges from typical phenomenological models. Our predictions for the branching ratios $ \mathcal{B}(V\to ggg) $ and $ \mathcal{B}(V\to e^+e^-) $ are in excellent agreement with experimental data. A striking feature is the distinct convergence behavior of the relativistic expansion: the leptonic width converges rapidly already at $ q^2 $ order, whereas the gluonic width converges far more slowly due to destructive interference induced by the nodal structure in the multi-gluon convolution. These results offer new insights into the dynamics of excited quarkonia and provide valuable constraints on non-perturbative descriptions of heavy-quark bound states.

In this paper, we first discuss the linear independence of the complete elliptic integrals of the first, second and third kinds $K(k)$, $E(k)$ and $Π(μ(k),k)$, and then obtain an upper bound for the number of zeros of a function of the form \begin{eqnarray*} p(k)K(k)+q(k)E(k)+r(k)Π(μ(k),k),\ k\in(-1,1), \end{eqnarray*} where $p(k)$, $q(k)$ and $r(k)$ are real polynomials, $μ(k)$ is a real polynomial or rational function. Finally, we apply it to a Hamiltonian triangle with three invariant straight lines under small real polynomials piecewise smooth perturbation.

Interplanetary networks (IPNs) present unique challenges such as extreme delay, high loss, and frequent disruptions that severely degrade the performance of conventional transport protocols like Transmission Control Protocol (TCP) and Quick UDP Internet Connection (QUIC). To address these issues, we propose a secure transport acceleration strategy tailored for IPNs. This strategy is founded on our Non-Transparent Secure Proxy (NTSP) architecture, which enables connection splitting for end-to-end encrypted flows while preserving application layer security. Based on the NTSP, we design an IPN-aware transport policy that combines (i) a rate-based congestion control algorithm exploiting the pre-scheduled nature of deep-space links to achieve stable and efficient bandwidth utilization, and (ii) an adaptive packet-level forward error correction scheme to provide low-latency loss recovery without retransmissions. Furthermore, we introduce a theoretically grounded backpressure flow control mechanism, deriving an analytical model for optimal buffer sizing to mitigate rate mismatch and prevent bufferbloat. The strategy is implemented in a prototype system, PEPspace, and evaluated in representative Earth-Moon scenarios. Results show near-capacity and stable goodput and substantially improved delivery performance compared with TCP/QUIC variants and existing Performance Enhancing Proxies, while maintaining low latency and robust data delivery across intermittent links. The NTSP architecture is further discussed as a foundational framework for future unified IP/DTN architectures, bridging a key architectural gap in heterogeneous space networks.

Scenarios where dark matter is boosted to relativistic velocities provide a promising probe of sub-GeV dark matter. Cosmic-ray upscattered and supernova-produced dark matter generate relativistic fluxes peaked toward the Galactic Centre, an anisotropy that offers a strong directional signature and is not mimicked by any terrestrial or cosmic background. We determine how many directional recoil events are required in a gas time-projection chamber to distinguish various scenarios for the origin of dark matter particles arriving in the solar system, which are otherwise indistinguishable without directionality. We find that standard halo dark matter particles can be distinguished from boosted populations with as few as $\mathcal{O}(20)$ events under reasonable track reconstruction performance and background conditions.

Magic angle twisted bilayer graphene (MATBG) hosts narrow moiré bands with meV-scale energy splittings, making its correlated phases sensitive to both material parameters and modeling choices in low-energy downfolding. We develop an ab initio quantum-embedding workflow that derives interacting flat-band Hamiltonians from Kohn-Sham density functional theory (KS-DFT) of a relaxed, unstrained structure. Our model combines constrained random phase approximation (cRPA) screening, controlled double-counting subtraction, and an automated gauge-fixing procedure based on the selected columns of the density matrix (SCDM) that is compatible with symmetry-resolved many-body calculations. Solving the resulting models using Hartree-Fock (HF) and coupled cluster singles and doubles (CCSD), we recover robust insulating Kramers intervalley coherent (KIVC) states at charge neutrality ($ν=0$) and at electron doping ($ν=+2$). The main new physical effect appears on the hole-doped side: at $ν=-2$ we observe a fragile semimetal with a weak $\sqrt{3}\times\sqrt{3}$ Kekulé modulation and enhanced intervalley-scattering peaks in the Fourier-transformed local density of states. Although the underlying KS-DFT band structure is nearly particle-hole symmetric, the effective interacting Hamiltonian exhibits a pronounced particle-hole asymmetry at $ν=\pm 2$ that we trace to momentum-dependent single-particle renormalizations generated by subtraction terms constructed from reference densities consistent with the KS-DFT filling. Our work provides a first-principles route for connecting microscopic electronic structure, screened interactions, subtraction choices, and scanning tunneling microscopy signatures in MATBG.

We consider the following graph reconstruction problem: given an unweighted connected graph $G = (V,E)$ with visible vertex set $V$ and an oracle which takes two vertices $u,v \in V$ and returns the shortest path distance between $u$ and $v$, how many queries are needed to reconstruct $E$? Specifically, we consider bounded degree $Δ$ and bounded treelength $\mathrm{tl}$ connected graphs and show that reconstruction can be done in $O_{Δ,\mathrm{tl}}(n \log n)$ queries with a deterministic algorithm. This result improves over the best known algorithm (deterministic or randomized) for this graph class by a $\log n$ factor and matches the known lower bound for the class of graphs with bounded chordality, which is a subclass of bounded treelength graphs.

We investigate, how finite temperature influences quantum coherence in multipartite open systems by analyzing a tripartite spin boson model subjected to non-Markovian dephasing. Two distinct environmental configurations are considered viz. independent local reservoir and a common structured reservoir characterized by an Ohmic spectral density. In this framework, temperature enters explicitly through the time dependent dephasing rates, enabling a systematic exploration of thermal effects on coherence dynamics. Using the relative entropy of coherence, we examine representative pure states belonging to inequivalent entanglement classes along with physically relevant mixed states constructed from them. Under local non-Markovian dephasing, all states exhibit monotonic coherence decay, with temperature acting as a universal accelerator of decoherence. In contrast, the common reservoir scenario reveals a strikingly non-universal behaviour. While $GHZ$ and $Star$ type states undergo temperature enhanced degradation, $W$ class states and certain Werner type mixtures display robust stationary coherence that remains largely insensitive to thermal fluctuations. These results demonstrate that the thermal susceptibility of coherence is governed not only by environmental configuration but also by the internal architecture of multipartite quantum states. The interplay between reservoir structure and state geometry leads to qualitatively distinct dynamical regimes ranging from rapid thermal fragility to temperature resilient coherence preservation. Our findings identify coherence dynamics as a sensitive probe of structured finite temperature environments and suggest a pathway toward coherence based quantum thermometry and nanoscale calorimetry using engineered multipartite states.

We study a class of degenerate hyperbolic equations in a bounded domain whose degeneracy occurs at a boundary point. We first develop the weighted functional framework, prove well-posedness of the degenerate problem, and establish regularity away from the degenerate point. We then introduce a shape-design approximation obtained by removing a small neighborhood of the degenerate boundary point, which yields uniformly non-degenerate hyperbolic problems on regularized domains. We prove that the regularized solutions converge to the solution of the original degenerate equation, including the convergence of the boundary normal derivatives away from the degenerate point. Finally, under a geometric condition on the observation boundary, we derive an observability inequality for the degenerate equation by combining the uniform observability of the regularized problems with the limit passage.

The global spin polarization of hyperons in heavy-ion collisions have been investigated by including spin correlation effects among their constituent quarks. The available data on global spin polarizations of hyperons and spin alignments of vector mesons provide constraints on phase space functions of the spin polarization and correlation. These constraints can lead to inequalities under some approximations, which might provide possible clues for the presence of quark spin correlation inside hyperons at lower collision energies.

This paper presents a novel wireless sensing system where a movable antenna (MA) continuously moves and receives sensing signals within a three-dimensional (3-D) region to enhance sensing performance compared with conventional fixed-position antenna (FPA)-based sensing. We show that the performance of direction vector estimation for a target is fundamentally related to the 3-D MA trajectory in terms of the mean square angular error lower-bound (MSAEB), which is adopted as a coordinate-invariant performance metric. In particular, the closed-form expression of the MSAEB is derived as a function of the trajectory covariance matrix. Theoretical analysis shows that two-dimensional (2-D) antenna movement suffers from performance divergence for target direction close to the endfire direction of the 2-D MA plane, whereas 3-D movement can achieve isotropic sensing performance over the entire angular region. To achieve robust sensing performance, we formulate a min-max optimization problem to minimize the maximum (worst-case) MSAEB over a given continuous angular region wherein the target is located. An efficient successive convex approximation (SCA) algorithm is developed to optimize the 3-D MA trajectory and obtain a locally optimal solution. Numerical results demonstrate that the proposed 3-D MA sensing scheme is able to significantly reduce the worst-case mean square angular error (MSAE) compared with conventional arrays with FPAs and MA systems with 2-D movement only, thus achieving more accurate and robust direction estimation over the entire angular region.

This paper presents CSST-PSFNet, a deep learning method for high-fidelity point spread function (PSF) reconstruction developed for the Chinese Space Station Survey Telescope (CSST). The model integrates a residual neural network, a lightweight Transformer architecture, and a variational latent representation to address key challenges in CSST imaging, including severe PSF undersampling, inter-band variability, and smooth spatial variation across the focal plane. Trained and validated on high-resolution star-PSF pairs generated by the CSST Main Survey Simulator, CSST-PSFNet achieves improved pixel-level accuracy and more precise recovery of shape parameters relevant to weak lensing compared to widely used PSFEx. On both the standard test dataset and a blurred dataset representing the upper bound of expected on-orbit PSF degradation, the model achieves a size residual precision below 0.005 and an ellipticity residual precision below 0.002. A weak-label adaptation experiment further shows that the model can recover PSFEx-level performance when the true PSF is unknown, demonstrating robustness in controlled degradation scenarios and weak-label adaptation experiments. These results indicate that CSST-PSFNet provides a flexible and extensible framework for future on-orbit PSF calibration in large-scale CSST surveys, with potential applications in weak-lensing cosmology and precision astrophysical measurements.

Let $H$ be an infinite-dimensional separable Hilbert space and let $(X,d,μ)$ be a metric measure space satisfying the doubling and upper Alhfors regularity conditions at small scale. We prove that every bounded continuous tight frame $Ψ\colon X\rightarrow H$ can be sampled to obtain a frame for $H$, which is uniformly discrete and nearly tight. That is, for every $0<ε<1$, there exist a sampling sequence $\{x_n\}_{n\in\mathbb{N}}$ in $X$ and $r>0$ such that $\inf_{n\neq m}d(x_n,x_m)\geq r$ and $\{Ψ(x_n)\}_{n\in\mathbb{N}}$ is a frame whose ratio of frame bounds is less than $1+ε$. We apply our main result to show that for every nonzero function $g$ in $L^2(\mathbb{R}^d)$ there exists a uniformly discrete set $Λ$ such that the corresponding Gabor system $\{e^{2πibx}g(x-a)\}_{(a,b)\in Λ}$ is a nearly tight frame. We also prove that if $ψ\in L^2(\mathbb{R})$ satisfies the Calderón admissibility condition, then there exists a uniformly discrete set $Γ$ such that wavelet system $\{a^{1/2}ψ(ax-b)\}_{(a,b)\in Γ}$ is a nearly tight frame. Analogous discretization results for exponential frames and spectral subspaces of elliptic differential operators are presented as well.

In this paper, we introduce Spyglass, a spectrum sensor designed to address the challenges of effective spectrum usage in dense wireless environments. Spyglass is capable of observing a frequency band and accurately estimating the Angle of Arrival (AoA) of any signal during a single transmission. This includes additional signal context such as center frequency, bandwidth, and I/Q samples. We overcome challenges such as the clutter of fleeting transmissions in common bands, the high cost of array processing for AoA estimation, and the difficulty of detecting and estimating channels for unknown signals. Our first contribution is the development of Searchlite, a protocol-agnostic signal detection and separation algorithm. We use a switched array to reduce cost and processing complexity, and we develop SSFP, a signal processing technique using Fourier transforms that is synchronized to switching boundaries. Spyglass performs multi-channel blind AoA estimation synchronized with the array. Implemented using commercially available hardware, Spyglass demonstrates a median AoA accuracy of 1.4$^\circ$ and the ability to separate simultaneous signals from multiple devices in an unconstrained RF environment, providing valuable tools for large-scale RF data collection and analysis.

We investigate the commutativity and semi-commutativity of generalized singular integral operators of the form $P_{+} f P_{+} + P_{-} g P_{+} + P_{+} u P_{-} + P_{-} v P_{-}$ on $L^{2}$, where $P_{+}$ denotes the Riesz projection and $P_{-}=I-P_{+}$. Building on this analysis, we develop a unified approach to studying the algebraic properties of operator classes on $L^{2}$ generated by multiplication operators together with the Riesz projection. These classes include, but are not limited to, Toeplitz+Hankel operators, singular integral operators, Foguel--Hankel operators, and asymmetric dual truncated Toeplitz operators. We provide complete characterizations of (i) the quasinormality of singular integral operators, and (ii) the necessary and sufficient conditions under which the product of two asymmetric dual truncated Toeplitz operators is again an asymmetric dual truncated Toeplitz operator. In addition, our methods provide new proofs of several known results, including the classical Brown-Halmos theorems and the commutativity of Hankel operators, singular integral operators, and dual truncated Toeplitz operators. We also improve the conditions for the normality of singular integral operators.

In this paper, we present an alternate, elementary proof of the local Lipschitz regularity of the suitable weak solution of heat flow of harmonic maps into CAT(0)-metric spaces, whose existence was established by Lin, Segatti, Sire, and Wang through an elliptic regularization approach. The ideas of the proof are inspired by Korevaar and Schoen, and they work for any CAT(0)-metric space $(X,d)$ as the target and any complete Riemanan manifold $(M,g)$, with positive injectivity radius and bounded curvature, as the domain.

Generative Retrieval (GR) has emerged as a promising paradigm to unify indexing and search within a single probabilistic framework. However, existing approaches suffer from two intrinsic conflicts: (1) an Optimization Blockage, where the non-differentiable nature of discrete indexing creates a gradient blockage, decoupling index construction from the downstream retrieval objective; and (2) a Geometric Conflict, where standard unnormalized inner-product objectives induce norm-inflation instability, causing popular "hub" items to geometrically overshadow relevant long-tail items. To systematically resolve these misalignments, we propose Differentiable Geometric Indexing (DGI). First, to bridge the optimization gap, DGI enforces Operational Unification. It employs Soft Teacher Forcing via Gumbel-Softmax to establish a fully differentiable pathway, combined with Symmetric Weight Sharing to effectively align the quantizer's indexing space with the retriever's decoding space. Second, to restore geometric fidelity, DGI introduces Isotropic Geometric Optimization. We replace inner-product logits with scaled cosine similarity on the unit hypersphere to effectively decouple popularity bias from semantic relevance. Extensive experiments on large-scale industry search datasets and online e-commerce platform demonstrate that DGI outperforms competitive sparse, dense, and generative baselines. Notably, DGI exhibits superior robustness in long-tail scenarios, validating the necessity of harmonizing structural differentiability with geometric isotropy.

We show that the full-flag Johnson graph has spectral gap equal to that of its Schreier quotient arising from the point-stabiliser equitable partition. Our results confirm two conjectures posed by Huang, Huang, and Cioabă, which imply an Aldous-type spectral-gap phenomenon for full-flag Johnson graphs.

In this paper, under the extremely mild assumption $u(x)= O(|x|^{K})$ as $|x|\rightarrow+\infty$ for some $K\gg1$ arbitrarily large, we classify solutions of the following mixed order conformally invariant system with exponentially increasing and nonlocal nonlinearities in $\mathbb{R}^{n}$: $$ \left\{ \begin{aligned} (-Δ)^{\frac{1}{2}}u & = e^{pv} \\ (-Δ)^{\frac{n}{2}}v & = \left(\frac{1}{|x|^2}*u^2\right)u^2 \end{aligned} \right. \quad \text{in}\; \mathbb{R}^n, $$ where $n=3,\,4$, $p>0$, $u\geqslant0$, $v(x)=o(|x|^2)$ as $|x|\to\infty$ and $u$ satisfies the finite total mass condition. The finite total mass condition can be deduced from either $u \in L^\frac{2n}{n-1}(\mathbb{R}^n)$ or $u \in \dot{H}^\frac{1}{2}(\mathbb{R}^n)$. This system is closely related to the conformally invariant equations $(-Δ)^{\frac{1}{2}}u=\left(\frac{1}{|x|^{2}}*u^2\right)u$ and $(-Δ)^{\frac{n}{2}}u=(n-1)!e^{nu}$ in $\mathbb{R}^{n}$ with $n=3,4$, which have been quite extensively studied.

Accurate prediction of fragmentation cross sections is essential for rare-isotope beam production, planning new-isotope searches, and designing experiments to study the most exotic regions of the nuclear chart. However, existing reaction models and phenomenological cross-section parameterizations often exhibit significant deviations over broad regions of mass and charge. In this work, a Bayesian-inspired model-averaging framework is developed to combine abrasion--ablation (AA) calculations based on multiple nuclear mass tables into a single statistically weighted estimate. For the calibrated systems, the model weights are assigned empirically according to the relative quality of fit to measured cross sections, thereby reducing systematic model bias while preserving the underlying physics content of the AA description. The weights are constrained using proton-rich fragmentation data for the $^{78}$Kr and $^{124}$Xe projectiles. The resulting parameter trends are then propagated to the $^{92}$Mo and $^{144}$Sm systems through a controlled scaling procedure. In the present implementation, the excitation-energy prescription is fixed, while the averaging is performed across nuclear-mass inputs; the framework provides both weighted cross sections and associated uncertainty estimates. Applied to proton-rich fragmentation, the present approach provides a practical basis for interpolation and limited extrapolation in regions relevant to rare-isotope production. The resulting predictions are used to assess the production of very proton-rich nuclei, and candidate new isotopes are discussed.

In today's in-person group discussions, smartphones are integrated as intelligent workstations; yet given their co-presence in such face-to-face interactions, whether and how they may enhance people's behavioral engagement with others remains underexplored. This work investigates how animating personal smartphones to move expressively, without compromising regular functions, can transform them into active embodied facilitators for co-located group interaction. In the four-stranger small-group discussion setting, guided by Tuckman's group-development theory, we conducted a design workshop (n=12) to identify problematic group-work circumstances and design expressive, attention-efficient animated phone facilitations. Subsequently, we developed AnimaStand, a movement-enabled phone stand that animates phones to deliver group facilitation cues according to conversation dynamics. In a between-subjects Wizard-of-Oz study (n=56) with four-stranger group discussions, where everyone's phone was on an AnimaStand, the facilitations re-engaged inactive members, enhancing group dynamics, task operation performance, and relationships. We finally discuss prospects for more adaptive and generalizable animated device personal facilitation.