Multipole expansion methods have been primarily used for analyzing the electromagnetic scattering from non-magnetic isotropic dielectric scatterers, and studies about the scattering from magnetic objects seem to be lacking. In this work, we used the multipolar expansion framework for decomposing the electromagnetic scattering by dielectric particles with magnetic properties. Magnetization current contributions were explicitly accounted for by using the vector spherical harmonics to compute the electric and magnetic multipole contributions of arbitrary order. The exact analytical expressions for the corresponding spherical multipole coefficients were employed, with the scattering efficiencies being used to distinguish the dielectric and magnetic contributions of each multipole. This enables the analysis of scattering from arbitrarily shaped, anisotropic, and inhomogeneous magnetic scatterers. It also provides a tool for studying non-reciprocal devices that exploit magnetic resonances in magnetic-dielectric materials. Calculations were made for an experimentally feasible system, namely for ferrite-based scatterers operating in the microwave regime. These materials are of interest in radio frequency (RF) applications due to their magnetic activity. We demonstrated analytically that the magnetic circular dichroism in a magnetic-dielectric scatterer in the Faraday geometry can be decomposed into individual multipole contributions. The analytical results indicate that multipole resonances associated with magnetization currents can be even stronger than multipole contributions from conventional dielectric currents. It is worth noting that these analytical results were verified through comparison with numerical results from finite element method (FEM) simulations in COMSOL Multiphysics.

Cross-DNS: Domain resolution with a reputation-driven cross-chain framework for global expansion

Abstract Machine learning interatomic potentials (MLIPs) enable accurate atomistic modeling, but reliable uncertainty quantification (UQ) remains elusive. In this study, we investigate two UQ strategies, ensemble learning and D-optimality, within the atomic cluster expansion framework. It is revealed that higher model accuracy strengthens the correlation between predicted uncertainties and actual errors and improves novelty detection, with D-optimality yielding more conservative estimates. Both methods deliver well calibrated uncertainties on homogeneous training sets, yet they underpredict errors and exhibit reduced novelty sensitivity on heterogeneous datasets. To address this limitation, we introduce clustering enhanced local D-optimality, which partitions configuration space into clusters during training and applies D-optimality within each cluster. This approach substantially improves the detection of novel atomic environments in heterogeneous datasets. Our findings clarify the roles of model fidelity and data heterogeneity in UQ performance and provide a practical route to robust active learning and adaptive sampling strategies for MLIP development.

Abstract Foundational machine learning interatomic potentials that can accurately and efficiently model a vast range of materials are critical for accelerating atomistic discovery. We introduce universal potentials based on the graph atomic cluster expansion (GRACE) framework, trained on several of the largest available materials datasets. Through comprehensive benchmarks, we demonstrate that the GRACE models establish a new Pareto front for accuracy versus efficiency among foundational interatomic potentials. We further showcase their exceptional versatility by adapting them to specialized tasks and simpler architectures via fine-tuning and knowledge distillation, achieving high accuracy while preventing catastrophic forgetting. This work establishes GRACE as a robust and adaptable foundation for the next generation of atomistic modeling, enabling high-fidelity simulations across the periodic table.

Policymakers today face many, interrelated uncertainties. In addition, they have to strike a balance between efficiency, cost-effectiveness, and overarching social objectives. Addressing these problems requires a coupling of several approaches. Thus, we model the power generation expansion planning (PGEP) problem as a combined simulation-optimization problem. Since agent-based simulations (ABM) are able to effectively represent markets, we formulate the PGEP as a multi-stage multi-scale mixed-integer linear optimization problem, where the results of the ABM are integrated into a stochastic optimization model using affine cuts. First, we propose a double decomposition framework combining Benders decomposition and stochastic dual dynamic programming (SDDP) algorithms to solve the PGEP problem. Second, we couple the stochastic optimization model with an agent-based electricity market simulation (AMIRIS) to evaluate power portfolio decisions from a market perspective. We discuss the process of extracting dual values from agent-based simulations with the goal of calculating optimality cuts for the Benders decomposition, to incorporate the simulation results into the optimization model. In particular, we investigate three coupling strategies connecting the optimization and AMIRIS models. Our results show that integrated simulation-optimization approaches yield superior portfolio decisions using both centralized and decentralized operations. Furthermore, they combine recourse and wait-and-see solutions, enhancing resilience against uncertainties.

Abstract Australia’s current geographical indication (GI) framework primarily serves the wine industry under the Wine Australia Act 2018 (Cth). This limited application overlooks the broader potential of GIs to protect and promote a diverse range of origin-linked products, from regional foods to artisanal crafts. In contrast, trade marks dominate Australia’s IP landscape, offering individual brand protection but lacking the communal, place-based identity that GIs uniquely safeguard. Drawing on the European Union’s (EU’s) expansive GI framework, the article highlights recent developments in the EU’s protection of non-agricultural products, such as craft and industrial goods, under its new regulation for craft and industrial GIs. This progression reflects the EU’s strategic recognition of how GIs can foster local economies, preserve cultural traditions and enhance global competitiveness. This article also reviews the Australia-European Union Free Trade Agreement, explaining where the negotiations have reached and the regained momentum for finalizing the Agreement. It discusses the highlights of this Agreement and the possibilities it provides for Australia to expand its GI framework beyond wine and grape products. Framed around the theme of crafting identity, this article invites readers to reimagine Australia’s approach to GIs. It argues for a more sophisticated GI framework that truly embraces the full spectrum of products anchored in place. Through comparative analysis, legal critique and policy insight, the article will explore how Australia can better protect its cultural fabric and unlock new opportunities in regional branding and international trade.

Rydberg atom arrays have emerged as a powerful platform to simulate a number of exotic quantum ground states and phase transitions. To verify these capabilities numerically, we develop a versatile quantum Monte Carlo sampling technique which operates in the reduced Hilbert space generated by enforcing the constraint of a Rydberg blockade. We use the framework of stochastic series expansion and show that in the restricted space, the configuration space of operator strings can be understood as a hard rod gas in d+1 d + 1 dimensions. We use this mapping to develop cluster algorithms which can be visualized as various non-local movements of rods. We study the efficiency of each of our updates individually and collectively. To elucidate the utility of the algorithm, we show that it can efficiently generate the phase diagram of a Rydberg atom array, to temperatures much smaller than all energy scales involved, on a kagomé link lattice. This is of broad interest as the presence of a Z_2 Z 2 spin liquid has been hypothesized recently.

In many land cover classification tasks, the limited precision of individual sensors hinders the accurate separation of certain classes, largely due to the complexity of the Earth’s surface morphology. To mitigate these issues, decision fusion methodologies are employed, allowing data from multiple sensors to be synthesized into robust and more conclusive classification outcomes. This study employs fully polarimetric Synthetic Aperture Radar (PolSAR) imagery and leverages the strengths of three decomposition methods, namely Pauli’s, Krogager’s, and Cloude’s, by extracting their respective components for improved detection. From each decomposition method, three scattering components are derived, enabling the extraction of informative features that describe the scattering behavior associated with various land cover types. The extracted scattering features, treated as independent sensors, were used to train three neural network classifiers. The resulting outputs were then considered as local decisions for each land cover type and subsequently fused through a decision fusion rule to generate more complete and accurate classification results. Experimental results demonstrate that the proposed Multi-Class Bahadur–Lazarsfeld Expansion (MC-BLE) fusion significantly enhances classification performance, achieving an overall accuracy (OA) of 95.78% and a Kappa coefficient of 0.94. Compared to individual classification methods, the fusion notably improved per-class accuracy, particularly for complex land cover boundaries. The core innovation of this work is the transformation of the Bahadur–Lazarsfeld Expansion (BLE), originally designed for binary decision fusion into a multi-class framework capable of addressing multiple land cover types, resulting in a more effective and reliable decision fusion strategy.

Medication recommendation aims to generate treatment regimens that balance efficacy and safety based on patients' historical medical records. Recent studies leveraging longitudinal Electronic Health Records (EHR) sequence modeling have significantly improved recommendation accuracy. However, existing methods still face two main limitations: on the encoder side, most approaches rely solely on sequence models to learn patient representations, failing to adequately mine the structured medical knowledge implicit in EHR; on the decoder side, methods employing a "copy-and-add" recommendation strategy attempt to simulate clinical decision-making but generally lack explicit modeling of long-tail drug distributions and temporal dynamics. To address these issues, we propose HeteroMed, a medication recommendation model enhanced by heterogeneous graph knowledge. The model constructs a multi-relational medical heterogeneous graph covering diagnosis, procedure, and drug entities to effectively integrate the structured cross-entity medical knowledge from EHR. It employs a gating mechanism to achieve dynamic knowledge fusion, thereby mitigating risks from noise and distribution shift. In the decoding stage, HeteroMed incorporates temporal factors and proposes a collaborative drug expansion and inheritance framework to model the increases and decreases in patient medication usage, achieving the appropriate introduction of potentially effective new drugs while retaining suitable existing ones. Furthermore, the model introduces an expected Drug-Drug Interaction (DDI) regularizer to enhance the safety of the recommended drug combination. Experimental results on two public datasets demonstrate that HeteroMed outperforms multiple representative baseline models on various metrics, and exhibits a stronger balancing capability between prescription efficacy and safety.

Abstract This study examines the dual-edged role of digital finance (DF) and regional innovation (RI) in shaping corporate excess leverage (CEL) within China’s swiftly evolving economic landscape. Drawing on a comprehensive panel dataset of 1200 firm-year observations from Chinese listed firms (2011–2020). Combining theories of optimal capital structure, credit market dynamics and systemic risk, we employ fixed effects, two-stage least squares, system GMM and quantile regression techniques. The findings reveal a nuanced paradox: while digital finance significantly expands credit access, it simultaneously exacerbates corporate leverage, challenging the narrative that technological innovations invariably democratize financial markets. Moreover, regional innovation, contrary to Schumpeterian expectations, fails to independently mitigate leverage risk without robust institutional frameworks, exposing systemic vulnerabilities in debt-driven ecosystems. Notably, our analysis reveals significant heterogeneity across ownership structures: state-owned enterprises (SOEs) exploit regional innovation for policy-driven objectives and escalate leverage, whereas private firms (POEs) face heightened risks from unregulated DF due to agency costs and weaker safeguards. The study advances existing theoretical perspectives by (1) bridging the credit expansion and financial constraint framework; (2) refining the resource-based review, highlighting that state-backed resources distort innovation‒leverage dynamics, amplifying financial instability in SOEs; and (3) extending agency theory to the financial ecosystem, where regulatory asymmetries and information gaps intensify managerial risk-taking. Practically, we propose adaptive policies: AI-driven surveillance in innovation hubs for real-time risk mitigation and institutional capacity-building in underdeveloped regions to balance financial inclusion with stability.

ABSTRACTScleractinian corals are foundational to coral reefs, vital marine ecosystems under threat from climate change. Montipora, a widely distributed reef‐building genus, contributes through continuous corallum mineralization, yet polyp budding and skeleton formation processes remain elusive. This study elucidates temporal and spatial dynamics of skeletal formation and polyp budding in Montipora capricornis using high‐resolution micro‐computed tomography (micro‐CT). We demonstrate that skeleton–canal network formation precedes polyp budding at colony margins, identifying a “transit area” (volumes ~1 mm3, skeleton‐to‐void ratio 20%–35%) within tubular canals as a pathway for polyp migration to new calices. This feature serves as a morphological budding marker, enabling visualization of polyp trajectories and growth axes. The polyp‐canal system undergoes dynamic changes, including concurrent skeleton formation and dissolution. These insights establish a structural framework for biomineralization regulation and colony expansion, contributing to the development of coral growth models, and informing environmental impacts on reef‐building in M. capricornis.

India’s Act East Policy (AEP), inaugurated in 2014, represents a strategic reorientation of the nation’s foreign policy architecture, aimed at cultivating deeper political, economic and civilisational engagements with the Asia-Pacific region. While dominant narratives surrounding the AEP have largely emphasised geostrategic calculus and economic connectivity, this study interrogates a less explored yet profoundly significant dimension: the cultural and intellectual potential of the policy, situated within the expansive framework of global humanities. Anchoring the analysis in North-East India – a region imbued with intricate historical, linguistic and ethno-cultural affiliations with Southeast Asia – the paper elucidates how this borderland frontier emerges as a pivotal actor in advancing India’s soft power and transnational outreach. Through a multidisciplinary approach that synthesises cultural diplomacy, postcolonial theory and regional integration studies, the research foregrounds the transformative capacity of cultural exchanges, scholarly collaboration, and shared historical memory in reconfiguring regional identities. The paper contends that for India’s AEP to achieve its full strategic and diplomatic potential, it must embrace the cultural and humanistic affinities that animate North-East India’s enduring linkages with Southeast Asia, thus repositioning the region as a geopolitical transit zone and a dynamic cultural interlocutor in India’s evolving Indo-Pacific engagement.

In this work, we propose an exact analytical description of acoustic frozen waves (AFWs) within the framework of partial wave expansion. An AFW is mathematically constructed from a discrete or continuous one-dimensional superposition of colinear Bessel beams, therefore, constituting an important type of longitudinally structured acoustic threads. The exact determination of the expansion coefficients-known as the beam shape coefficients-offers a fundamental tool for applications such as acoustic radiation force and scattering calculations for structured beams. We provide examples of reconstructions that might be useful in several applications, ranging from acoustic tweezers, volumetric displays, acoustic holography, medical imaging, biomedical ultrasonics, and cell treatment, among others.

Abstract: This article examines Seongho Yi Ik's reinterpretation of the Four-Seven debate, focusing on his engagement with both Neo-Confucian thought and Western theories of the soul and physiology. While initially defending Toegye's perspective, Seongho diverged from traditional frameworks by integrating Matteo Ricci's soul theory and Schall von Bell's physiological insights. He redefined xin (心, heart–mind) through the concept of zhijue (知覺, perception and sentience), distinguishing between cognition (知) and sensory perception (覺) by associating them with the xin as a heart organ and the brain, respectively. This shift allowed him to naturalize emotions and introduce gong (公, impartial) and si (私, partial) as moral criteria, moving beyond metaphysical explanations to a practical, socially expansive framework. Seongho's theory represents a pivotal transition in Joseon Neo-Confucianism, aligning with the broader intellectual trend toward practical learning ( silhak , 實學) in the late Joseon period.

The prospects for finding common ground between naturalists and Christian philosophers seems to be bleak. The typical naturalist is an anti-supernaturalist, the Christian philosopher would appear to be a supernaturalist par excellence, and we are told that these positions are mutually exclusive. An expansive naturalist framework calls into question this way of dividing up the philosophical territory and my initial task in this paper is to spell out the shape of this expansive naturalism, using Iris Murdoch as a key interlocuter. I examine her relation to the Christian theologian Paul Tillich and consider the implications for an assessment of her commitment to atheism. I argue that there is a knife-edge between their respective positions and that this has important implications for an understanding of the limits of expansive naturalism as well as the prospects for finding common groundbetween naturalists and Christian philosophers. ---------------------------------------- Received: 13/09/2025. Reviewed: 15/10/2025. Accepted: 18/11/2025.

This study investigates the experiential characteristics and limitations of XR (Extended Reality)–based exhibitions and explores the potential for expanding these experiences through Generative Artificial Intelligence (Generative AI). To this end, a case study was conducted on domestic and international XR exhibitions implemented between 2015 and 2025, drawing on literature reviews and visitor feedback. The collected cases were classified into five types according to their functions and experiential structures: spatial guidance, informational assistance, participatory interaction, narrative extension, and preservation or restoration. Based on Csikszentmihalyi’s flow theory, the study examined how XR exhibitions foster sensory and emotional immersion, and theoretically discussed how generative AI may further enhance these experiential qualities. The results indicate that although XR exhibitions demonstrate strengths in visual realism and spatial augmentation, they also exhibit common limitations across types, including insufficient context-aware interaction, restricted emotional and cognitive engagement, and largely fixed narrative structures. In response, this study proposes the potential roles of generative AI in enabling real-time information adaptation, dynamic narrative generation, and personalized interaction based on visitors’ linguistic, behavioral, and affective data. The study ultimately presents an integrated “XR Exhibition Experience Expansion Framework” that links XR exhibition types, immersion elements, and AI application strategies. This framework provides future directions for experience design and technological convergence in the field of art exhibitions.

Abstract We present an optimised multipole algorithm for computing the three-point correlation function (3PCF), tailored for application to large-scale cosmological datasets. The algorithm builds on an in situ interpretation of correlation functions, wherein spatial displacements are implemented via translation window functions. In Fourier space, these translations correspond to plane waves, whose decomposition into spherical harmonics naturally leads to a multipole expansion framework for the 3PCF. To accelerate computation, we incorporate density field reconstruction within the framework of multiresolution analysis, enabling efficient summation using either grid-based or particle-based schemes. In addition to the shared computational cost of reconstructing the multipole-decomposed density fields—scaling as $\mathcal {O}(L^2_{\textrm{trun}} N_g \log N_g)$ (where Ng is the number of grids and Ltrun is the truncation order of the multipole expansion)—the final summation step achieves a complexity of $\mathcal {O}(D^6_{\textrm{sup}} N_g)$ for the grid-based approach and $\mathcal {O}(D^3_{\textrm{sup}} N_p)$ for the particle-based scheme (where Dsup is the support of the basis function and Np is the number of particles). The proposed in situ multipole algorithm is fully GPU-accelerated and implemented in the open-source Hermes toolkit for cosmic statistics. This development enables fast, scalable higher-order clustering analyses for large-volume datasets from current and upcoming cosmological surveys such as Euclid, DESI, LSST, and CSST.

A new framework for cosmological expansion in a reformulated Newtonian-Like gravity with variable G

Abstract To achieve accurate rate of rock hardness recognition in coal mine roadway excavation, support, and anchor operations, vibration and acoustic signals during the cutting process of rocks with different hardness is collected using a self-developed experimental platform for rock hardness recognition in Excavation-Support-Anchor equipment. A cross-modal rock hardness recognition method based on Hyperbolic Tangent Adaptive Space Learning (HTASL) is proposed. The method first segments the collected vibration and acoustic signals into fixed-length sample segments to construct an initial rock hardness sample set. Time-frequency domain features of each sample are extracted to form a 27-dimensional feature vector that characterizes rock hardness properties. Aiming at the problems of sample structural scatter deviation and feature space distortion caused by noise and redundant information interference during cross-modal feature fusion, the sample structural scatter of rock hardness is obtained through modal expansion. A label-aware adaptive module is designed to constrain the geometric relationship of samples, constructing a feature space that integrates latent structures and label information, and establishing a local adaptive structural scatter to solve the problem of local distortion in the feature space. Meanwhile, we design a hyperbolic tangent structural scatter correction mechanism. The sample structural scatter is decomposed into singular vectors and singular values, and the hyperbolic tangent structural scatter is reconstructed after correcting the singular values through hyperbolic tangent constraints. This scatter is integrated into the modal expansion framework to build the HTASL model. Through theoretical derivation, the analytical solution of the projection direction is obtained, realizing cross-modal rock hardness feature extraction with strong class separability. The effectiveness of the HTASL method is verified by comparative experiments and ablation experiments on the dataset from the self-developed Excavation-Support-Anchor experimental platform.

Abstract Stellarators are inherently steady-state and disruption-free. However, three-dimensional geometry significantly increases the complexity of both equilibrium calculation and configuration optimization. While global three-dimensional magnetohydrodynamic equilibria are generally solved numerically, asymptotic expansions provide a powerful tool for studying stellarator configurations. Despite successes with near-axis expansion, the expansion near an arbitrary flux surface presents unresolved theoretical challenges. We investigate the vacuum field constraints in Boozer coordinates and develop a near-surface expansion framework. This approach is validated on a rotating elliptical boundary stellarator and a precise quasiaxisymmetric stellarator. The results from the first-order expansion agree with the global solutions in high precision, demonstrating a quadratic convergence rate as theoretically predicted. Using a recursive first-order expansion, we can derive approximate global solutions that are considerably far away from the reference surface but remain consistent with the 3D MHD equilibrium code. The shape of the magnetic axis can also be quickly estimated with a few recursive calculations. This work generalizes the asymptotic expansion method to an arbitrary flux surface and provides a new perspective for stellarator configuration studies.