We investigate collective dynamics in a pulse-coupled adaptive Winfree network under the influence of a frustration (phase-lag) parameter. The coupling strengths coevolve according to a Hebbian adaptation rule and self-organize to support a wide variety of collective states. We observe frequency-clustered states, entrainment, bump states, bump--frequency cluster states, antipodal and multi-antipodal cluster states, chimera states, and incoherent dynamics. Notably, we report for the first time the spontaneous emergence of entrainment, bump, and bump--frequency cluster states in an adaptive network {\it without} any external forcing. To systematically characterize these regimes, we introduce three complementary measures of incoherence based on (i) time-averaged frequencies, (ii) instantaneous phases, and (iii) mean frequencies per bin. These measures enable the construction of one- and two-parameter phase diagrams that clearly delineate transitions between distinct dynamical states. Furthermore, we analytically derive the stability condition for the frequency-entrained state, which shows excellent agreement with numerical simulations. Our results highlight the crucial role of frustration-mediated plasticity in shaping rich self-organized dynamics in pulse-coupled adaptive networks.

We investigate a gauge transformation based on the successive elimination of time-dependent onsite potentials at individual sites in finite or infinite systems. Our analysis shows that this transformation renormalizes the inward hoppings by a phase factor $e^{i ϕ(t)}$ and the outward hoppings by $e^{-i ϕ(t)}$. We further demonstrate how this procedure facilitates the reduction and simulation of pulse propagation in scattering systems, while significantly simplifying the time-ordered integrals involved in the time evolution operator for time-dependent Schrodinger equation.

In this paper, we address the problem of distributed state estimation for a discrete-time, linear time-invariant system. Building on the framework proposed in [2], we exploit the Jordan canonical form of the system matrix to develop a distributed estimation scheme that ensures the asymptotic convergence of the local state estimates to the true system state. The proposed approach relies on the idea that each node reconstructs the components of the system state that are detectable for it through a local Luenberger observer, while employing a consensus-based strategy to estimate the undetectable components. Necessary and sufficient conditions for the existence of a distributed observer that guarantees asymptotic estimation accuracy are derived. Compared with the previous work [2], the proposed design offers greater flexibility in the selection of the coupling gains and leads to a less restrictive set of conditions for solvability.

Target shape, not just size, plays a pivotal role in determining detectability during random search. We analyze intermittent Lévy walks in three dimensions, and mathematically prove that the widely observed Cauchy strategy (Lévy exponent $μ= 2$) uniquely achieves scale-invariant, near-optimal detection across a broad spectrum of target sizes and shapes. In a domain of volume $n$ with boundary conditions, expected detection time for a convex target of surface area $Δ$ optimally scales as $n/Δ$. Conversely, Lévy strategies with $μ< 2$ are slow at detecting targets with large surface area-to-volume ratios, while those with $μ> 2$ excel at finding large elongated shapes but degrade as targets become wider. Our results further indicate a continuous geometric transition: volume dictates detection near $μ= 1$, ceding dominance to surface area as $μ\to 2$, after which surface area and elongation couple to govern detection. Ultimately, 3D search introduces a pronounced sensitivity to target shape that is absent in lower dimensions. Our work provides a rigorous foundation for the Lévy flight foraging hypothesis in 3D by establishing the scale-invariant optimality of the Cauchy walk. Furthermore, our results reveal dimensionality-driven shape vulnerabilities and offer testable predictions for biological and engineered systems.

Exceptional points (EPs), indicative of parity-time (PT) symmetry breaking, play a central role in non-Hermitian physics, yet most studies begin from deliberately engineered effective Hamiltonians whose parameters are tuned to exhibit exceptional behavior. In realistic open quantum systems, however, dynamics are governed by Lindblad superoperators whose spectral structure is high-dimensional, symmetry-constrained, and not obviously reducible to minimal non-Hermitian models. A general framework for discovering exceptional dynamics directly from microscopic dissipative models has been lacking. Here we introduce a symmetry-resolved approach for identifying and characterizing exceptional points directly from the full Liouvillian generator. Correlated dissipation induces graph symmetries that decompose Liouville space into low-dimensional invariant sectors, within which minimal non-Hermitian blocks govern the onset of EPs and PT-breaking behavior. We further introduce a numerical diagnostic - the exceptional-point strength $\mathcal{E}$ - based on eigenvector conditioning, which quantifies proximity to defective dynamics without requiring analytic reduction. Applied to tight-binding models with correlated dephasing and relaxation, the method reproduces analytically predicted exceptional seams and reveals universal scaling of $\mathcal{E}$ near EP manifolds. More broadly, the framework enables systematic discovery of hidden exceptional structure in complex or high-dimensional open systems and is naturally compatible with matrix-free and tensor-network implementations for scalable many-body applications.

Breakthrough technologies increasingly shape social institutions, economic systems, and political futures. Yet models of research excellence associated with such technologies often prioritize technical performance, scalability, and short-term innovation metrics while treating ethical, social, and cultural dimensions as secondary considerations. This perspective article argues that such separation is no longer tenable. We propose a broader understanding of excellence that combines technical rigor with ethical robustness, social intelligibility, and long-term relevance. The rapid emergence of generative and agentic artificial intelligence further underscores this argument. As technological systems increasingly operate through language, interpretation, and normative alignment, expertise traditionally cultivated in the humanities and social sciences becomes integral to the design, governance, and responsible deployment of such systems. Drawing on historical examples and contemporary research practices, this article examines five interconnected domains where the humanities and social sciences, treated as integrated dimensions of research practice, can strengthen technological development: (1) ethical, legal, and social integration in agenda-setting and research design; (2) plural and reflexive foresight practices that shape technological futures; (3) graduate education as a leverage point for cross-disciplinary literacy; (4) visualization and communication as epistemic and civic practices; and (5) institutional frameworks that move beyond rigid distinctions between basic and applied research. Across these dimensions, we propose practical strategies for embedding interdisciplinary collaboration structurally rather than symbolically.

We investigate symmetric edge polytopes generated by Erdős--Rényi random graphs in a high-dimensional regime. These objects provide a natural and largely unexplored model of random lattice polytopes, in which geometric properties are governed by graph-theoretic structure. Focusing on the number of polytope edges and on the number of edges in unimodular triangulations, we derive precise asymptotics for expectations and variances and establish central limit theorems with explicit rates of convergence. Our analysis combines a detailed combinatorial-geometric study of the graph configurations determining the facial structure with the discrete Malliavin--Stein method for normal approximation. In particular, we identify a distinguished parameter value at which the leading variance term cancels, producing an atypical fluctuation regime. To the best of our knowledge, the results obtained here constitute the first distributional limit theorems for random lattice polytopes

We establish Anderson localization for long-range quasi-periodic operators with large trigonometric potentials and Diophantine frequencies, the proof is based on a novel dynamical rigidity argument.

Quantum fluctuation relations provide a microscopic formulation of thermodynamics beyond equilibrium, but experimentally accessing many-body quantum work statistics remains an outstanding challenge. The quantum piston constitutes a canonical model of boundary-driven nonequilibrium dynamics, where finite-time deformation of a confining potential generates non-adiabatic transitions, dissipation and irreversibility. Here we experimentally simulate the nonequilibrium dynamics of a two-boson quantum piston on a programmable photonic quantum computer. Using two indistinguishable photons, we encode a truncated piston propagator through a quasi-unitary embedding, with an ancilla mode representing leakage into higher-energy states outside the resolved manifold. This architecture enables direct reconstruction of thermodynamic transition statistics for both expansion and compression protocols as functions of driving speed and final trap length. We observe the crossover from quasi-adiabatic to strongly non-adiabatic evolution and show that bosonic interference restructures the resulting two-particle Fock-state populations and work distributions. The measured statistics are in close agreement with theoretical predictions and satisfy the Jarzynski equality across expansion and compression protocols for cyclic driving we further quantify irreversibility through dissipated work and state overlap. Our work identifies programmable photonic quantum hardware as a powerful platform for simulating nonequilibrium quantum thermodynamics and for experimentally resolving how indistinguishability and many-body interference shape quantum work, dissipation and entropy production.

Environmental, Social, and Governance (ESG) standards have been increasingly adopted by organizations to demonstrate accountability towards ethical, social, and sustainability goals. However, generating ESG reports that align with these standards remains challenging due to unstructured data formats, inconsistent terminology, and complex requirements. Existing ESG lifecycles provide guidance for structuring ESG reports but lack the automation, adaptability, and continuous feedback mechanisms needed to address these challenges. To bridge this gap, we introduce an agentic ESG lifecycle framework that systematically integrates the ESG stages of identification, measurement, reporting, engagement, and improvement. In this framework, multiple AI agents extract ESG information, verify ESG performance, and update ESG reports based on organisational outcomes. By embedding agentic components within the ESG lifecycle, the proposed framework transforms ESG from a static reporting process into a dynamic, accountable, and adaptive system for sustainability governance. We further define the technical requirements and quality attributes needed to support four main ESG tasks, such as report validation, multi-report comparison, report generation, and knowledge-base maintenance, and propose three architectural approaches, namely single-model, single-agent, and multi-agent, for addressing these tasks. The source code and data for the prototype of these approaches are available at https://gitlab.com/for_peer_review-group/esg_assistant.

A critical point metric is a critical point of the total scalar curvature functional restricted to the space of constant scalar curvature metrics on a closed manifold with unit volume. It was conjectured in 1980's that every critical point metric must be Einstein. In this paper, we prove that this conjecture is true if the norm of the traceless Ricci operator $|\widetilde{Ric}|$ is constant. For $3$-dimensional case, we prove that the conjecture is true, if the traceless Ricci operator satisfies $tr((\widetilde{Ric})^3)\geq -\frac{R}{12}|\widetilde{Ric}|^2$, where $R$ denotes the scalar curvature. where R denotes the scalar curvature.

We use the complexity function of an invariant, not necessary closed, subset of a two-sided shift space to compute the polynomial entropy of the induced dynamics on the hyperspace of continua for certain one-dimensional dynamical systems. We also provide a simple criterion for $f$ that implies $C(f)$ has infinite topological entropy.

Plasma composition in flaring regions has been shown to have significant spatial and temporal variations, likely driven by dynamical processes that take place as a consequence of the sudden energy release at the reconnection site. The origins of these variations, as well as the effects they might, in turn, have on flare loops dynamics are not yet fully understood. In this work, we investigate the link between flare loop cooling times and plasma composition evolution in the loops formed during the M-class flare peaking at 13:56 UT on the 2022 April 2 using high cadence Hinode EIS spectroscopic observations. The analysis focuses on quantifying the cooling rate (using a series of emission lines covering a wide temperature range) and plasma composition evolution (using the Ca XIV 193.866 A/Ar XIV 194.401 A diagnostic) at the apex and footpoint of the flare loop arcade. Results show slower cooling and a FIP bias of 2.4 +/- 0.2 in the loop footpoint and faster cooling and a stronger FIP bias of 2.8 +/- 0.2 in the loop apex. The potential effects of plasma composition changes on the radiative cooling process of flare loops are also investigated by comparing observed loop cooling times to those predicted by simulations from the EBTEL 0D hydrodynamic model. The EBTEL simulations show that an higher FIP bias would lead to a faster radiative cooling rate and, therefore, shorter cooling times. This suggests that the variation in FIP bias observed in the two features could be responsible for the different cooling times observed.

Many practical optimization problems involve objective function values that are corrupted by unavoidable numerical errors. In smooth nonconvex optimization, quasi-Newton methods combined with line search are widely used due to their efficiency and scalability. These methods implicitly assume accurate function evaluations and thus may fail to converge in noisy settings. Developing fast and robust quasi-Newton methods for such scenarios is therefore crucial. To address this issue, we propose a noise-tolerant regularized quasi-Newton method equipped with a relaxed Armijo-type line search, designed to remain stable under inaccurate function evaluations. By combining a regularization parameter update rule inspired by Objective-Function-Free Optimization and the AdaGrad-Norm method, we establish a global convergence rate of $\mathcal{O}(1/\varepsilon^2)$ for reaching a first-order stationary point under the assumed error model. We performed extensive experiments on the CUTEst benchmark collection with artificially noisy objective function evaluations, as well as with low-precision floating-point arithmetic (64-, 32-, and 16-bit). The results demonstrate that the proposed method is substantially more robust than several existing methods, while maintaining competitive practical convergence speed and computational cost.

This paper presents massively parallel computation (MPC) algorithms in the strongly sublinear memory regime (aka, scalable MPC) for orienting and coloring graphs as a function of its subgraph density. Our algorithms run in $poly(\log\log n)$ rounds and compute an orientation of the edges with maximum outdegree $O(α\log\log n)$ as well as a coloring of the vertices with $O(α\log\log n)$ colors. Here, $α$ denotes the density of the densest subgraph. Our algorithm's round complexity is notable because it breaks the $\tildeΘ(\sqrt{\log n})$ barrier, which applied to the previously best known density-dependent orientation algorithm [Ghaffari, Lattanzi, and Mitrovic ICML'19] and is common to many other scalable MPC algorithms.

In networks-on-chip, static routing schemes are favored for their simplicity and predictability, but they cannot effectively balance network load due to the unawareness of runtime load distribution. Q-StaR discovers two factors (topology and traffic distribution) that determine the long-term trend of load distribution, and proposes N-Rank to extract this trend. The obtained information is used to guide BiDOR's route selection at runtime, thereby improving load balancing while retaining simplicity and predictability. Simulation validates that Q-StaR significantly outperforms the typical dimension-order routing (throughput under uniform traffic improved by 42.9\%, and mean/maximum latency under realistic workloads reduced by 86.4\%/95.3\%).

Recently, a number of methods have emerged to detect galaxy clusters solely through their weak lensing signal. Using the recently-introduced wavelet multi-scale detection method, we focus here on the potential for the use of tomographic information of the source galaxies to increase the number of weak lensing detections. We apply the $z_{s,\mathrm{min}}$-cut technique, consisting of the combination of weak lensing peak detections emerging from lensing maps obtained using different source redshift bins, to mock data sets of progressively increasing sophistication. The source redshift distribution is chosen to be $Euclid$-like, with a maximum depth of $z_{s,\mathrm{max}}=3$, and overlapping tomographic redshift bins are constructed by progressively increasing the minimum source redshift $z_{s,\mathrm{min}}$. Considering all possible detection combinations from one to four tomographic bins, we find that a single source redshift bin, with $z_{s,\mathrm{min}}=0.4$, performs as well as the combination of multiple redshift bins. By running detections on synthetic clusters of varying complexity -- from isolated Navarro Frenk White haloes to haloes embedded in and formed within N-body cosmological simulations, and considering both true and photometric source redshifts -- we show that while large-scale structure contamination and photometric redshift errors reduce the potential gains of the tomographic approach, the dominant limitation is the accumulation of spurious detections across redshift bins, leading to decreased purity at a fixed detection threshold.

Cell switching is a promising approach for improving energy efficiency in wireless networks; however, existing studies largely rely on simplified models and energy-centric formulations that overlook key performance-limiting factors. This paper revisits the cell switching concept by redefining its modeling assumptions and mathematical formulation, explicitly incorporating realistic propagation effects such as building entry loss (BEL) and atmospheric losses relevant to non-terrestrial networks (NTN), particularly high-altitude platform station (HAPS). Beyond proposing a new cell switching strategy, the conventional energy-focused problem is reformulated as a multi-objective optimization framework that jointly minimizes power consumption, unconnected users, and data rate degradation. Through this reformulation, the proposed methods ensure that energy-efficient operation is achieved without compromising user connectivity and data rate performance, thereby inherently supporting sustainability objectives for sixth-generation (6G) networks. To solve this reformulated problem, two complementary approaches are employed: the weighted sum method (WSM), which enables flexible and adaptive weighting mechanism, and the {ε-constraint-inspired method (εCM), which converts connectivity and rate-related objectives into constraints within the conventional energy-focused problem. Moreover, unlike prior work relying only on simulations, this study combines system-level simulations with Sionna-OpenAirInterface (OAI) based emulation on a smaller network to validate the proposed cell switching concept under realistic conditions. The results show that, compared to the conventional approach, WSM reduces rate degradation for up to 70% for high-loss indoor users and eliminates the 44% drop for low-loss indoor users.

We consider the class of closed Riemannian $n$-manifolds with Ricci curvature and injectivity radius bounded below by uniform constants, and an upper bound on the diameter. We establish a uniform upper bound for the eigenvalues of the Hodge Laplacian acting on differential forms on Riemannian manifolds in this class, similar to the classical eigenvalue comparison theorem proved by Cheng for the Laplace-Beltrami operator acting on smooth functions. This extends earlier work of Dodziuk and Lott, which required sectional curvature bounds in addition to bounds on other geometric quantities. As an application, we obtain uniform eigenvalue estimates for the connection Laplacian acting on $1$-forms.

We present a deterministic framework for proton therapy dose calculation based on finite element discretizations of the energy-dependent $M_1$ moment model. The nonlinear $M_1$ system is derived from the Fokker--Planck equation for charged particles and closed using an entropy-based approximation of the second moment. Energy is treated as a pseudo-time coordinate. The zeroth and first moments of the proton fluence are evolved backward in energy. To ensure hyperbolicity and physical admissibility, we employ a monolithic convex limiting (MCL) strategy. Representing the standard continuous Galerkin discretization in terms of auxiliary `bar' states, we construct a nonlinear scheme that is provably invariant domain preserving (IDP) w.r.t. convex realizable sets consisting of all admissible states. The realizability of the bar states is enforced using the MCL technology for homogeneous hyperbolic systems. The forcing induced by stiff scattering is incorporated using Strang-type operator splitting. We use an explicit strong-stability-preserving Runge--Kutta method for the radiation transport subproblem and exact integration in the forcing steps, which guarantees the IDP property. The deposited dose is defined as the integral of a weighted zeroth moment over a bounded energy range. It is accumulated during the backward-in-energy evolution. Numerical experiments demonstrate that the proposed Strang-MCL method produces accurate and physically consistent dose distributions.

We report laser cooling of Yb3+-doped KY3F10 (Yb:KY3F10) driven by a 100-W, 1020-nm pump source. Despite pumping at a non-optimal wavelength, high-quality KY3F10 crystals doped with 3% and 7% Yb were cooled to 145 K and 151 K, respectively, in a double-pass pump configuration. These results establish Yb:KY3F10 as an attractive laser-cooling medium competitive with Yb:YLF, the state-of-the-art laser-cooling material for optical cryocoolers. The observed cooling performance and spectroscopic characteristics suggest that lower cryogenic temperatures may be achieved through pump-wavelength optimization, enhanced pump absorption, and reduced radiative heating.

Over-the-air (OTA) emulation of diverse sensing target characteristics in a controlled laboratory environment is pivotal for advancing integrated sensing and communication (ISAC) technology, as it facilitates the non-invasive performance evaluation of ISAC base stations (BSs) across complex scenarios. In this work, a flexible multi-target OTA emulation framework based on a wireless cable method is proposed to evaluate the sensing performance of large-scale ISAC BSs. The core concept leverages an amplitude and phase modulation (APM) network to simultaneously establish wireless cables and simulate target spatial characteristics without consuming additional resources on costly radar target emulators. For the wireless cable method, the condition number increases as the number of antennas scales up, which affects the performance of the wireless cable. Although the wireless cable concept has been established for devices-under-test (DUTs) with a limited number of antenna ports, establishing wireless cables for large-scale DUTs remains an open question in the community. We address this problem by optimizing the OTA probe array configuration based on the theoretical properties of strictly diagonally dominant matrices. Experimental results validate the proposed framework, demonstrating high-isolation wireless cables for a 32-element DUT and an extremely low condition number for a 128-element synthetic array. Furthermore, the OTA emulation of a dynamic dual-drone scenario confirms the method's effectiveness and practicality in reproducing complex sensing environments.

We study the spin-dependent cross section in semi-inclusive deep-inelastic lepton-nucleon collisions, $\ell + N^\uparrow\to \ell+h+X$. We focus on the cross section that is integrated over the transverse momentum $P_{h\perp}$ of the detected unpolarized hadron. We analyze this cross section at large virtuality $Q^2$ of the exchanged virtual photon within the framework of collinear twist-3 factorization in perturbative QCD. The two main transverse spin observables, the single nucleon spin asymmetry (SSA) and the double longitudinal lepton-transverse nucleon spin asymmetry (DSA), both receive contributions from chiral-odd twist-3 three-parton fragmentation functions. We study these fragmentation contributions to Next-To-Leading Order (NLO) in perturbative QCD. We explicitly observe that collinear twist-3 factorization holds for these contributions at the one-loop level. We confront our NLO formulae with HERMES data and provide numerical predictions for EIC kinematics.

We compute the $RO(C_p \times C_p)$-graded Bredon cohomology of equivariant universal and classifying spaces associated to families of subgroups, with coefficients in the constant Mackey functor $\underline{\mathbb{F}_p}$. An explicit description of the resulting coefficient ring, including its multiplicative structure, is obtained. These computations are then applied to the study of lifts of cohomology operations via the Bredon cohomology of equivariant complex projective spaces.

We introduce a new class of interacting dark sector models that couple the intrinsic entropy of dark matter to scalar field dark energy. Using the Lagrangian formulation for relativistic perfect fluids, we construct consistent covariant actions that incorporate algebraic and derivative entropy couplings. These interactions leave the expansion history unchanged, rendering the background cosmology indistinguishable from $Λ$CDM or uncoupled quintessence. At the level of cosmological perturbations, the entropy couplings generate scale-dependent modifications to the dark matter Euler equation, while the continuity equation remains unaltered at linear order. The resulting interactions correspond to a pure-momentum exchange within the dark sector. We show that intrinsic entropy perturbations can carry primordial scale dependence, and non-minimal couplings can lead to a scale-dependent suppression or enhancement of structure growth. Finally, we demonstrate that these models are generically compatible with current Cosmic Microwave Background observations, while inducing distinctive signatures in large-scale structure. The framework provides a theoretically well-motivated and observationally viable extension to the standard cosmological model, opening new directions to explore novel interactions in the dark sector.

One-body multipole operators are defined as irreducible representations of rotational symmetry together with spatial-inversion and time-reversal symmetries, providing a systematic framework for classifying electronic internal degrees of freedom and for describing a wide variety of composite order parameters. While this formalism has been successfully established for the one-body operator space, a systematic classification of the many-body operator space, especially in interacting systems, remains an open challenge. In this paper, we extend the multipole formalism in the one-body operator space to the many-body operator space. By formulating fermionic creation and annihilation operators as spherical tensors and employing Clebsch-Gordan coupling combined with the exterior (Grassmann) algebra, we construct an irreducible decomposition of many-body operators that fully incorporates fermionic antisymmetrization. As a concrete application, we classify monopoles appearing in spinless many-body operators. In particular, we show that the electric toroidal monopole, a pseudoscalar breaking spatial-inversion symmetry, and the magnetic toroidal monopole, a time reversal-odd scalar, become active in spinless interacting many-body systems, although they are absent in the spinless one-body hybrid orbital space.

Orbital angular momentum (OAM) entanglement gives access to multiple qubit and high dimensional Hilbert spaces, but is unfortunately susceptible to disturbance, decaying in real-world noisy channels. Here, we show there is an underlying topology arising from OAM entanglement that is robust to such channels, which we demonstrate using atmospheric turbulence -- exemplary of stochastic or chaotic media. Using a quantum channel with various turbulence strengths, we find the OAM topological observable preserved even though the OAM itself is shown to be highly sensitive to the turbulence. We show this is true for mixed states too, with the OAM topology intact even as the purity of the state decreases due to decoherence. Our work offers a new perspective on OAM entanglement preservation, and may easily be extended to other spatial bases, degrees of freedom, as well as complex channels, whether static or dynamic.

We investigate cohomological invariants and motivic invariants of semisimple algebraic groups arising in the Freudenthal magic square. Besides, we show that if the Rost invariant of a strongly inner group of type $E_7$ is a sum of at most two symbols modulo 2, then it is isotropic over an odd degree field extension, and use this fact to give a different proof of a result of Petrov and Rigby. Moreover, we construct a cohomological invariant of degree 5 for certain groups of type $^2E_6$ which detects their isotropy.

We predict the tetragonal Mn2PC monolayer to be a room-temperature ferromagnetic half-metal with a Curie temperature of 554 K. The spin-up channel hosts type-II Weyl-like crossings at the Fermi level with highly anisotropic band dispersion, whereas the spin-down channel is a wide-gap semiconductor. Topological edge states obtained from tight-binding calculations confirm the non-trivial bulk topology. Spin-orbit coupling opens a small gap of 11.2 meV at the Weyl-like crossings, generating pronounced Berry curvature and a sizable anomalous Hall conductivity near the Fermi level. Based on these properties, we propose topological tunneling magnetoresistance in a Mn2PC-based magnetic tunnel junction: the parallel configuration conducts through fully spin-polarized Weyl-like carriers, while the antiparallel configuration is suppressed by the half-metallic gap, yielding a giant magnetoresistance ratio. The concurrent anomalous Hall effect in the conducting state provides an experimentally accessible signature of the topological carriers. These results identify the Mn2PC monolayer as a promising platform for room-temperature topological spintronic devices.

Half-Heusler compounds from the $RE$PtBi family exemplify Weyl semimetals in which external magnetic field induce Weyl nodes. These materials exceptionally host topologically non-trivial states near the Fermi level and their manifestation can be clearly seen in the magnetotransport properties. In this study, we tune the Fermi level of the archetypal half-Heusler Weyl semimetal GdPtBi through high-energy electron irradiation, moving it away from the Weyl nodes to investigate the resilience of the contribution of topologically non-trivial states to magnetotransport properties. Remarkably, we observe that the negative longitudinal magnetoresistance, which is a definitive indicator of the chiral magnetic anomaly occurring in topological semimetals, persists even when the Fermi level is shifted by 100\,meV from its original position in the pristine sample. Additionally, the anomalous Hall effect shows complex variations as the Fermi level is altered, attributed to the energy-dependent nature of the Berry curvature, which arises from avoided band crossing. Our findings show the robust influence of Weyl nodes on the magneto-transport properties of GdPtBi, irrespective of the Fermi level position, a behaviour likely applicable to many half-Heusler Weyl semimetals.