<?xml version="1.0" encoding="utf-8"?>
    <rss version="2.0">
      <channel xmlns:content="http://purl.org/rss/1.0/modules/content/">
        <title>Frontiers in Physics | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/physics</link>
        <description>RSS Feed for Frontiers in Physics | New and Recent Articles</description>
        <language>en-us</language>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-06-24T09:58:08.668+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1848401</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1848401</link>
        <title><![CDATA[The effects of Higgs boson couplings through HZZ production at future lepton colliders]]></title>
        <pubdate>2026-06-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Serdar Spor</author>
        <description><![CDATA[IntroductionWe focus on the sensitivity of the anomalous Higgs-gauge boson couplings at HγZ and HZZ vertices through the process ℓ−ℓ+→HZZ at CLIC and Muon Collider.MethodsSignal and relevant backgrounds events are generated in MadGraph within Standard Model Effective Field Theory (SMEFT) framework. These events are passed through Pythia for parton showering, and realistic detector effects are simulated by Delphes. The limits at 95% C.L. on the coefficients cHB and cHW are obtained at two b-tagging working points; loose and medium containing the Delphes card from CLIC and Muon Collider, corresponding to b-tagging efficiencies of 90% and 70%, respectively.ResultsWe report that our best 95% C.L. limits on cHB and cHW coefficients are [−0.00138;0.00090] and [−0.00162;0.00026], respectively, at 3 TeV CLIC with an integrated luminosity of 5 ab–1, and [−0.00024;0.00023] and [−0.00020;0.00009], respectively, at 10 TeV Muon Collider with an integrated luminosity of 10 ab–1.DiscussionThese limits are compared with the present experimental and various phenomenological limits.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1865258</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1865258</link>
        <title><![CDATA[A quad-band MEMS-switched frequency-reconfigurable slot antenna on liquid crystal polymer substrate for Sub-THz wireless communication]]></title>
        <pubdate>2026-06-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jacob Wekalao</author><author>Amuthakkannan Rajakannu</author><author>Tobias Topisia</author>
        <description><![CDATA[IntroductionThe rollout of 6G wireless systems demands compact, frequency-agile antennas capable of operating across multiple millimeter-wave bands to support diverse use cases such as wearable modules, reconfigurable intelligent surfaces, and small-cell infrastructure. Conventional fixed-band antennas struggle to meet these multi-band requirements without increasing device footprint or complexity, motivating the need for reconfigurable architectures that can dynamically switch between frequency states while maintaining high gain and efficiency on a low-loss, flexible substrate suited to sub-terahertz operation.MethodsA quad-band frequency-reconfigurable slot antenna was designed on a liquid crystal polymer substrate, incorporating four RF MEMS cantilever switches loaded across a complementary split-ring slot resonator. Five independent switching states were defined to control current-path routing across the resonator, enabling discrete reconfiguration among four millimeter-wave bands spanning 26.5–27.8, 37.2–38.6, 47.4–48.9, and 55.1–57.3 GHz. Full-wave electromagnetic simulations were used to characterize gain, return loss, and radiation efficiency for each switching configuration, and results were validated against experimental measurements. A machine learning surrogate model was additionally trained on physics-informed synthetic data to predict S-parameters and gain, intended to reduce the computational burden of iterative full-wave simulation during early-stage design.ResultsThe proposed antenna achieved realized gains of 8.74–12.86 dBi across all four bands, with return loss consistently exceeding 15 dB and radiation efficiency above 86% in every configuration. Simulated and measured results showed close agreement, with a mean absolute error of 0.41 dB and a maximum gain deviation of 0.44 dBi. The antenna's area-normalized gain efficiency exceeded that of contemporary reconfigurable designs evaluated for comparison. The machine learning surrogate model predicted S-parameters and gain with a mean absolute error below 0.5 dB.DiscussionThe strong agreement between simulated and measured performance, combined with the antenna's compact footprint and high gain-to-area ratio, supports its viability for 6G applications requiring multi-band agility within tight space constraints, including wearable modules, reconfigurable intelligent surfaces, and small-cell infrastructure. The accuracy of the physics-informed ML surrogate model further suggests a practical pathway for accelerating iterative antenna design by reducing reliance on computationally intensive full-wave simulations, offering a complementary tool for rapid performance estimation in early design stages.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1828984</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1828984</link>
        <title><![CDATA[Microscopic characteristics and macroscopic elastic properties of compositional disordered disk packings]]></title>
        <pubdate>2026-06-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Hao Liu</author><author>Xinggang Zhang</author><author>Yanhui Liu</author>
        <description><![CDATA[The macroscopic elastic response of disordered particulate systems is jointly governed by their microstructural organization and contact interactions. Based on this idea, the deformation gradient tensor is introduced to characterize the microscopic displacement field of disks in two-dimensional packings under affine deformation. By performing a second-order expansion of the potential energy density with respect to the squared interparticle distance, a unified expression for the elastic tensor of general packings under the affine assumption is established. For a linear contact model, analytical expressions for the bulk and shear moduli are derived, and by incorporating contact-type statistics and probability distributions in stiffness-disordered systems, a theoretical prediction is constructed for the continuous variation of the moduli with compositional disorder Rs and stiffness disorder Rk. Discrete Element Method (DEM) simulations show that both moduli decrease nonlinearly as disorder increases, while the affine theory consistently provides an upper-bound estimate. The deviation between affine predictions and simulated responses serves to quantify non-affine deformation effects, which are most pronounced at intermediate Rs and low Rk. To the best of our knowledge, this study represents the first systematic investigation-integrating affine theoretical analysis with DEM simulations-into how stiffness disorder governs the force networks and elastic moduli in compositional disordered disk packings, thereby establishing a unified framework for understanding and rationally tuning the mechanical response of disordered soft materials.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1833425</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1833425</link>
        <title><![CDATA[Dynamic reactive power optimization and cooperative control for distribution and transmission power grids based on multi-agent deep reinforcement learning]]></title>
        <pubdate>2026-06-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Weiyi Li</author><author>Liping Shen</author><author>Yang Cheng</author><author>Xu Yang</author>
        <description><![CDATA[The high time-variability and multi-dimensional coupling characteristics of distribution and transmission power grids impose severe challenges on conventional reactive power optimization methods, which generally fail to balance real-time response performance and global optimality, thereby easily causing voltage limit violations and excessive network power losses. To address this issue, this paper proposes a dynamic reactive power optimization and collaborative control framework based on multi-agent deep reinforcement learning (MADRL). A constrained collaborative optimization model is established with the objectives of minimizing network loss and regulation cost, while satisfying power flow balance, voltage magnitude and line transmission security constraints. The multi-agent interactive learning process is formulated as a Markov game, where an attention communication module is embedded to reduce information interaction overhead, and an adversarial perturbation framework is introduced to enhance operational adaptability under stochastic and extreme grid disturbances. All hyperparameters are explicitly defined with reasonable selection principles and validated via sensitivity analysis to guarantee model reproducibility. Based on the Deep Deterministic Policy Gradient (DDPG) paradigm, the training efficiency and convergence stability are further improved. Comprehensive simulations are carried out on IEEE 33-bus and 69-bus medium-voltage distribution network benchmarks, as well as the IEEE 118-bus high-voltage transmission network benchmark, under diversified scenarios with varying distributed power penetration levels and random load fluctuations. Quantitative statistical comparisons in terms of voltage deviation, convergence performance and operational economy demonstrate that the proposed method outperforms traditional scheduling strategies and existing intelligent optimization algorithms in minimizing voltage deviation, maximizing cumulative rewards, and improving voltage control rates. Quantitative comparison results show that the proposed method reduces the mean voltage deviation by 85.0% compared with the state-of-the-art MADDPG baseline, achieving significantly higher voltage control accuracy than existing mainstream algorithms. The results verify the excellent collaborative coordination capability, anti-disturbance robustness and promising real-time application potential of the developed framework for medium- and large-scale distribution and transmission power grid operation scenarios.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1719822</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1719822</link>
        <title><![CDATA[Multi-modal dynamic radiography using short-pulse laser-generated probe beams]]></title>
        <pubdate>2026-06-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>S. H. Batha</author><author>M. Alvarado Alvarez</author><author>B. W. Bell</author><author>D. P. Broughton</author><author>P. Chu</author><author>S. De</author><author>A. Favalli</author><author>C.-K. Huang</author><author>A. Koskelo</author><author>T. R. Schmidt</author><author>B. T. Wolfe</author><author>Z. Wang</author><author>C.-S. Wong</author><author>R. E. Reinovsky</author>
        <description><![CDATA[Radiography is an important tool for the interrogation of dynamic experiments in the fields of dynamic properties of materials, and in condensed matter, high explosive, and high-energy-density physics. Multi-modal radiography advances the hypothesis that combining the information delivered by multiple radiographic modalities can lead to more constrained (improved) “reconstruction” of the scene than can be obtained from a single probe. We identify four modalities: multi-probe, time sequence, multi-view, and multi-messenger. Multi-probe radiography is a promising candidate for a next-generation dynamic radiographic facility. High-energy X-rays are the most frequently used probe for dynamic radiography, although recent developments show the utility of proton (pRad), electron (eRad), and neutron probe beams. Because each probing species interacts with material in the radiographic scene through quantitatively different mechanisms, each returns independent information about the scene, which can add extra constraints to the reconstruction process. How to conduct detailed, quantitative “co-analysis” of multiple data streams remains an area of active research. Multi-beam, short-pulse, laser-generated probes offer sufficient dose, an appropriate spectrum, and appropriate spatio-temporal resolution to produce high-quality dynamic radiographs. This paper reports on technology development to advance the state of the art of multi-modal/multi-probe radiography and the pursuit of both deterministic and inferential (AI/ML assisted) co-analysis methodologies to produce more constrained reconstructions from multi-modal data.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1835575</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1835575</link>
        <title><![CDATA[Dual magnetron Co-sputtering deposition: an effective approach for obtaining different phases of bismuth molybdate thin films]]></title>
        <pubdate>2026-06-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>R. González-Campuzano</author><author>D. E. Martínez-Lara</author><author>A. Hernández-Gordillo</author><author>S. E. Rodil</author>
        <description><![CDATA[The development of efficient, visible-light-responsive semiconductors is critical for advancing technologies in environmental remediation, gas sensing, and optoelectronics. Among promising candidates, ternary systems based on bismuth, molybdenum, and oxygen (Bi-Mo-O) have attracted considerable interest. These bismuth molybdates (BMOs) exhibit a wide variety of tunable compositions and crystalline phases, yielding excellent chemical, optical, and physical properties. Furthermore, synthesizing these materials as thin films is an essential step for their practical integration into functional devices and scalable manufacturing. This study details the synthesis and characterization of BMOs in thin-film form via dual-confocal magnetron sputtering, using two independent targets: α-Bi2O3 and Mo. To precisely alter the film composition, the power applied to the Mo target varied between 20 and 60 W, while the power to the Bi2O3 target was kept constant at 30 W. The films were subsequently annealed in an extra-dry air environment at 400 °C for 30 min to facilitate crystallization. Both as-deposited and annealed films were thoroughly characterized using mechanical and optical profilometry to understand their growth dynamics. The film composition was analyzed using energy-dispersive X-ray spectroscopy (EDX) and X-ray photoelectron spectroscopy (XPS), while the structural evolution was evaluated via X-ray diffraction (XRD). The EDX and XPS analyses revealed that the Bi/Mo atomic ratio decreased continuously as the power applied to the Mo target increased. Before annealing, the Bi/Mo ratio was generally higher, yielding films with Bi/Mo values ranging broadly from 4.6 to 0.2. The XRD results revealed the successful deposition of phase mixtures as well as isolated phases of BMO—including α-Bi2Mo3O12, β-Bi2Mo2O9, and γ-Bi2MoO6—alongside the solid solution Bi3.64Mo0.36O6.55 and localized bismuth oxides such as α-Bi2O3 and β-Bi2O3. Finally, the optical band gap of the BMO thin films was estimated considering an indirect fundamental inter-band transition, yielding values in the range of 2.49–3.03 eV, confirming their high suitability for visible-light-driven applications.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1755888</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1755888</link>
        <title><![CDATA[Threshold analysis of multi-group SIR models with heterogeneous mixing]]></title>
        <pubdate>2026-06-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Michele Bellingeri</author><author>Ayse Humeyra Bilge</author><author>Ayse Peker-Dobie</author><author>Sevgi Harman</author>
        <description><![CDATA[We analyze the spread of a social interaction agent of finite duration of interest, such as a petition, behavioral trend, or opinion, through a heterogeneous population using a multi-group SIR model. By integrating the model equations, we obtain explicit final-size relations and identify threshold conditions that determine whether propagation can be sustained. For one-, two-, and three-group systems, we show how within-group reinforcement and cross-group influence shape the geometry of the no-propagation boundary. The results provide a clear geometric characterization of heterogeneous diffusion and offer practical guidance for designing interventions that either promote or inhibit spread.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1700712</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1700712</link>
        <title><![CDATA[Artificial Leviathan: exploring social evolution of LLM agents through the lens of hobbesian social contract theory]]></title>
        <pubdate>2026-06-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Gordon Dai</author><author>Weijia Zhang</author><author>Jinhan Li</author><author>Siqi Yang</author><author>Chidera Onochie lbe</author><author>Srihas Rao</author><author>Arthur Caetano</author><author>Misha Sra</author>
        <description><![CDATA[IntroductionThe emergence of Large Language Models (LLMs) and advancements in Artificial Intelligence (AI) offer an opportunity for computational social science research at scale. Building upon prior explorations of LLM agent design, our work introduces a simulated agent society where complex social relationships dynamically form and evolve over time.MethodsAgents are given psychological drives and placed in a sandbox survival environment. We conduct an evaluation of the agent society through the lens of Thomas Hobbes’s seminal Social Contract Theory (SCT), analyzing whether agents seek to escape a brutish “state of nature” by surrendering rights to an absolute sovereign in exchange for order and security.ResultsIn our experiments, agents initially engage in unrestrained conflict, mirroring Hobbes’s depiction of the state of nature. However, as the simulation progresses, social contracts emerge, leading to the authorization of an absolute sovereign and the establishment of a peaceful commonwealth founded on mutual cooperation.DiscussionThe congruence between our LLM agent society’s evolutionary trajectory and Hobbes’s theoretical account indicates the capability of LLM’s to model intricate social dynamics that replicate forces which potentially shape human societies. By enabling insights into group behavior and emergent societal phenomena, LLM-driven multi-agent simulations hold potential for advancing our understanding of social structures, group dynamics, and complex human systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1901025</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1901025</link>
        <title><![CDATA[Editorial: Exploring human interactions through sociophysics: dynamics of opinion formation]]></title>
        <pubdate>2026-06-16T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Michele Bellingeri</author><author>Francisco Welington Lima</author><author>Alireza Abbasi</author><author>Roy Lindelauf</author><author>Valerio Restocchi</author><author>Xiu-Xiu Zhan</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1844769</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1844769</link>
        <title><![CDATA[Antimatter generation mechanism: a new perspective from entropy flow vector based on the quantum tensor network theory]]></title>
        <pubdate>2026-06-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yang Xiaodong</author><author>Yang Yuchen</author><author>Mei Helin</author>
        <description><![CDATA[IntroductionThe asymmetry between matter and antimatter in the universe is one of the most profound puzzles in modern physics. In this paper, we propose a unified framework based on entropy flow vector theory. Drawing on non-equilibrium thermodynamics, we adopt the entropy flow vector as a macroscopic variable describing the directional transport of entropy. Its covariant divergence gives the local entropy production rate, embodying the second law of thermodynamics in curved spacetime.MethodsWe establish a rigorous mathematical correspondence among entropy flow, quantum phase space, and quantum tensor networks, providing a microscopic foundation for the theory. The mechanism builds on rigorous results in open quantum systems showing that the time-evolution operator: (with weight) can be expanded in a complete basis containing both forward and backward modes.ResultsDuring local non-equilibrium processes in cosmic evolution, when the microscopic relaxation time is comparable to the macroscopic evolution time, a time-reversal branch can emerge in the evolution operator, with weight. Through the reversed action in the Feynman path integral, this corresponds mathematically to the production of antiparticles. Regions with reversed entropy flow naturally evolve into antimatter micro-domains. Landmark experiments on CP violation (Cronin--Fitch, 1964), T violation (CPLEAR, 1998), antihydrogen synthesis (PS210, 1995), vacuum quark--antiquark spin correlations (STAR, 2026), and antiproton coherence (BASE, 2025) verify, from different perspectives, the predicted resonance relaxation condition and time-reversal mechanism.DiscussionWe propose a laboratory-testable prediction: in an ultracold neutron--positron bound system, tuning the driving frequency to the system energy gap can induce local entropy flow reversal and generate detectable positron signals. This framework provides a new perspective on the matter-antimatter asymmetry and suggests concrete experimental avenues.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1844324</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1844324</link>
        <title><![CDATA[A superposed epoch of superDARN plasma convection, THEMIS all-sky auroral brightness, and their covariance during substorms]]></title>
        <pubdate>2026-06-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Matthew F. Wilcox</author><author>William A. Bristow</author>
        <description><![CDATA[Joule heating, driven by the interaction of electric fields and ionospheric conductivity, is a major source of thermospheric expansion during geomagnetic activity. Enhanced thermospheric density increases atmospheric drag on Low-Earth-Orbit (LEO) satellites, potentially shortening satellite lifetimes. Accurate estimates of Joule heating require reliable characterization of both plasma convection, which determines the electric field, and ionospheric conductivity, which is related to auroral brightness. In this study, we investigate the typical temporal and spatial evolution of plasma velocity, auroral brightness, and their covariance during auroral substorms. We performed a superposed epoch analysis of 10 auroral substorm events from solar cycle 24 using plasma convection measurements from the Super Dual Auroral Radar Network (SuperDARN) and auroral brightness observations from NASA’s Time History of Events and Macroscale Interactions during Substorms (THEMIS) All-Sky Imagers (ASI). The analysis examined the evolution of plasma velocity, auroral brightness, and their covariance relative to substorm onset. The superposed epoch results show that auroral brightening and expansion are broader and weaker than in individual events. Plasma velocities are reduced relative to individual-event magnitudes but persist westward and equatorward of onset. Negative covariance emerges near substorm onset and strengthens and expands with the regions of brightest auroral emission. These results capture the general behavior of plasma velocity, auroral brightness, and their covariance during substorms. Although not yet incorporated into empirical models, this covariance quantity may provide a practical constraint for predicting electric fields and conductivity in the auroral zone. Improved representation of these quantities could enhance Joule heating calculations, leading to better forecasts of thermospheric expansion and atmospheric drag on LEO satellites.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1841183</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1841183</link>
        <title><![CDATA[Optimized matching feature extraction method for bearing fault diagnosis]]></title>
        <pubdate>2026-06-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xuanming Cheng</author><author>Yuxing Li</author><author>Jingyi Li</author>
        <description><![CDATA[Feature extraction methods based on mode decomposition and dynamic metrics can characterize signal complexity in different frequency bands. However, existing methods usually ignore the complementarity among different metrics and the matching relationship between decomposed modes and metrics, which limits their generalization ability. To address these issues, this study proposes an optimized matching feature extraction method based on reconstructed complete ensemble empirical mode decomposition with adaptive noise (RCEEMDAN) and the optimized mode-metrics matching (OMMM) strategy. RCEEMDAN is developed to reduce redundant modes in CEEMDAN and obtain more accurate and refined modal components. OMMM is designed to determine the optimal matching relationship between decomposed modes and multi-source metrics. By introducing metrics generated from different evaluation mechanisms, OMMM enhances feature representation through metric complementarity. Simulation experiments demonstrate that RCEEMDAN achieves better decomposition performance than four comparison algorithms. Classification experiments on four types of chaotic signals further show that the proposed optimized matching feature extraction method provides superior classification performance compared with individual metrics. Real-world bearing signal experiments verify that the proposed method can effectively extract discriminative fault features. These results indicate that the integration of modes and metrics generated by different mechanisms improves the representation ability and generalization performance of feature extraction, making the proposed method effective for both simulated nonlinear signals and real bearing fault signals.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1820346</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1820346</link>
        <title><![CDATA[Riemannian Geometry Attention Heterogeneous Graph Network for complex networks: uncertainty modeling of signal propagation and cross-entity risk prediction in social and ESG governance networks]]></title>
        <pubdate>2026-06-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jixian Zhang</author>
        <description><![CDATA[With the development of green finance and the complexity of the social network ecosystem, ESG risk governance and social network risk prevention and control have become core issues in the intelligent management of complex networks. Both belong to typical complex heterogeneous networks and share common topological characteristics such as multi-agent interaction and uncertainty of signal propagation. However, their inherent heterogeneity and non-linear geometric structure make it difficult for traditional models to collaboratively capture the semantic correlation and geometric features of networks, which seriously restricts the accuracy of risk analysis. To this end, this paper proposes the Riemannian Geometry-Aware Heterogeneous Graph Network (RGA-HGN), which deeply integrates Riemannian geometry and heterogeneous graph attention mechanisms to realize the unified modeling of signal propagation uncertainty in social networks and ESG governance networks, as well as the accurate prediction of cross-agent risks. The model unifies multi-type node features through type-aware Euclidean embedding, retains the inherent geometric structure of the network by virtue of Riemannian manifold projection, and quantifies network risk correlation and captures the uncertainty of signal propagation using Riemannian geometry attention. Based on multi-source public data, a heterogeneous network of 30 Dow Jones enterprises is constructed, and the model is compared with 9 benchmark models on three core tasks. The results show that RGA-HGN significantly outperforms all baseline models. This study fills the research gap of geometric deep learning in the fusion analysis of social and ESG heterogeneous networks, and provides a universal and generalizable framework for complex network risk analysis.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1829651</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1829651</link>
        <title><![CDATA[Leveraging topological noise for dynamic state change detection using persistent homology]]></title>
        <pubdate>2026-06-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>B. Rishab Antosh</author><author>Sanjit Das</author><author>N. Nirmal Thyagu</author>
        <description><![CDATA[Characterization and classification of dynamical states of a system using persistent homology (PH) is proving to be rather fruitful in recent years. The marked success of the approach lies in mapping the topological features extracted by PH with the corresponding dynamical states of the system. However, there are two major drawbacks that cripple its usage widely. First, for effective identification of significant features extracted by the PH procedure, extensive human intervention and validation are inevitable. Second, identification and benchmarking of the periodic orbits via significant PH barcodes and Betti numbers stop short when the system transitions into a chaotic regime. In this paper, we address both the shortcomings of the PH procedure using a two-pronged approach. First, to minimize human intervention and validation, we employ a machine learning (ML) based binary classifier. We train the ML algorithm to demarcate true features from noise in the barcode data at a single instance of the dynamical parameter and allow the trained ML model to characterize the data at other parameter values. Second, we have proposed novel metrics derived from the insignificant feature count (short-lived features) that are normally discarded as noise. In this paper, we demonstrate that our metrics can clearly classify periodic and chaotic states as well as identify the parameters of dynamical transition sufficiently accurately, and compare their performance with a conventional method. We have assessed the performance of our metrics rigorously using three standard evaluators and benchmarked them against the maximum Lyapunov exponent, obtaining strong positive correlation coefficients ranging from 0.75 to 0.97. Furthermore, we assert that when the available data are sparse, as is typical in real-world systems, our proposed metrics can yield robust classification performance.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1842623</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1842623</link>
        <title><![CDATA[SH wave scattering and dynamic stress concentration in power-law graded materials with circular holes]]></title>
        <pubdate>2026-06-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Shiqiang Zhang</author><author>Zhenyu Wang</author><author>Tingting Liu</author><author>Ning Hu</author>
        <description><![CDATA[Functionally graded materials (FGMs) offer promising dynamic load mitigation capabilities, yet analytical solutions for elastic wave scattering around internal defects in graded media remain scarce. This study develops a two-dimensional analytical model for SH wave scattering by a circular hole in a power-law graded medium. Shear modulus and density are prescribed as proportional radial power-law functions with identical exponents, maintaining a spatially uniform wave velocity, which eliminates internal wave refraction, and any change in the dynamic stress concentration factor (DSCF) can be attributed exclusively to the spatial gradients of material stiffness and inertia. Analytical solutions are derived for the scattered wavefield and DSCF. Positive power-law gradients suppress the peak DSCF across all frequency bands, with the most pronounced reduction occurring under low-frequency excitation. Beyond a critical gradient threshold, the peak stress location shifts from the hole boundary into the surrounding matrix. These findings offer an analytical benchmark for the defect-tolerant design of power-law graded structures.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1872936</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1872936</link>
        <title><![CDATA[Editorial: Advanced signal processing techniques in radiation detection and imaging, volume II]]></title>
        <pubdate>2026-06-04T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Jian Dong</author><author>Zilong Liu</author><author>Yayun Cheng</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1744791</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1744791</link>
        <title><![CDATA[Quantum computing for loan portfolio pricing optimization]]></title>
        <pubdate>2026-06-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yanbo J. Wang</author><author>Xuan Yang</author><author>Chao Ju</author><author>Zhihang Liu</author><author>Jiawei Lu</author><author>Yue Zhang</author><author>Yiduo Wang</author><author>Xinkai Gao</author><author>Zijian Yu</author><author>Qi Xu</author><author>Xiaofeng Cao</author>
        <description><![CDATA[For complex optimization problems in the financial services industry, specialized quantum computer can server as a transformative tool. Focusing on loan portfolio pricing optimization, we demonstrate the application potentials and advantages of quantum computing in financial optimization problems. For the first time, the loan portfolio pricing problem is modeled as a Quadratic Unconstrained Binary Optimization (QUBO) problem, and then solved by a specialized quantum computer, Coherent Ising Machine (CIM) based on optical dissipative systems. By introducing an auxiliary qubit and conducting a two-stage search for the appropriate penalty coefficients, we demonstrate the applicability and advantages of CIM in the loan portfolio pricing optimization problem. The experimental results demonstrate that compared to state-of-the-art classical solvers, CIM achieves significant acceleration capability and energy efficiency beyond classical methods. Furthermore, by conducting numerical and analytical evaluations of CIM’s scalability in the loan portfolio pricing optimization, we demonstrate the convergence guarantees of CIM and its ability to achieve a better solution quality than the state-of-the-art classical solver in large-scale problems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1826826</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1826826</link>
        <title><![CDATA[Design and numerical analysis of a trapezoidal multilayer nano-wedge stack for dual-functional ultra-broadband absorption]]></title>
        <pubdate>2026-06-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Amuthakkannan Rajakannu</author><author>Jacob Wekalao</author>
        <description><![CDATA[IntroductionAchieving ultra-broadband, near-unity light absorption across both solar and thermal spectral ranges remains a critical challenge for next-generation energy harvesting and high-temperature thermal emission applications. Conventional absorber designs are limited by narrow spectral bandwidth, polarization sensitivity, and angular dependence, motivating the development of advanced nanostructured architectures capable of simultaneous dual-functional performance.MethodsThis study presents the computational design and rigorous numerical analysis of an asymmetrically-cascaded trapezoidal-profile multilayer nano-wedge absorber comprising tungsten (W), hafnium oxide (HfO2), and zirconium nitride (ZrN) thin films. The architecture features progressively tapered W/HfO2 bilayer units arranged in a unidirectional asymmetric cascade atop an optically opaque ZrN ground layer, forming a staircase-like ramp with non-centrosymmetric lateral periodicity. Systematic geometric parameter studies, material substitution screening, and structural configuration comparisons were conducted to identify optimal design parameters across the 300–4500 nm spectral range.ResultsThe optimized device achieves a spectrally averaged absorptivity of 97.2%, sustaining values above 90% over an ultra-broadband interval of 4200 nm. Evaluation under the AM1.5G solar spectrum yields a solar spectral conversion efficiency of 95.8%, corresponding to minimal optical losses of 4.2%. In Planckian thermal emission mode, near-ideal emissivities of 98.1% at 1000 K and 97.9% at 2000 K are observed, approaching the theoretical blackbody limit. Three distinct absorptivity resonance peaks at λ1 = 390 nm, λ2 = 920 nm, and λ3 = 3150 nm are identified, attributed respectively to a magnetic dipole resonance (MDR) in the top W nano-wedge layer, a guided cavity resonance (GCR) within W/HfO2/W Fabry–Pérot-type nanocavities, and a low-frequency inter-gap plasmonic resonance (IGPR) in deep-subwavelength lateral nanogaps. The device maintains absorptivity above 97% under TM-polarized incidence up to 40° and exhibits improved TE-polarized absorption at oblique angles.DiscussionW is identified as the optimal metallic constituent for maximizing dual-functional performance through material substitution screening. The complementary interplay of three distinct electromagnetic confinement mechanisms across the ultraviolet, near-infrared, and mid-infrared regions underpins the exceptional broadband response. The robust angular and polarization performance, combined with near-blackbody thermal emissivity at elevated temperatures, establishes the asymmetric-cascade nano-wedge design as a highly promising platform for solar energy harvesting and high-temperature selective thermal emission applications.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1824409</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1824409</link>
        <title><![CDATA[A security performance detection method for satellite space laser communication network based on transformer]]></title>
        <pubdate>2026-06-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Qing Zhong</author><author>Yinghu Wan</author><author>Xiaolin Zhu</author><author>Juan Zhu</author><author>Dejiang Wan</author><author>Yang Yu</author><author>Yao Jiang</author>
        <description><![CDATA[The satellite space laser communication network becomes the core support of the aerospace information interconnection system. However, the dynamic nature of its links and high-dimensional transmission of data poses diverse security threats to the network. In response to the above issues, this paper proposes a security performance detection method for satellite space laser communication network based on Trans-GAN-CBAM. This method combines generative adversarial thinking with Transformer architecture to learn the correlation characteristics between different types of satellite space laser communication network security abnormal behaviors. The core of embedding CBAM into Trans-GAN lies in the synergistic effect of channels and spatial attention modules to focus on the safety critical features in satellite space laser communication network. This effectively suppresses irrelevant information such as channel interference and invalid transmission, optimizing the process of extracting and characterizing security features. The experimental results indicate that Trans-GAN-CBAM further improves the accuracy of network security performance detection. It realizes the detection of security anomalies in satellite space laser communication network and provides support for optimizing network ecurity performance.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1824578</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1824578</link>
        <title><![CDATA[Design and implementation of machine learning-based anomaly detection in the ITER Tokamak Systems Monitor]]></title>
        <pubdate>2026-06-01T00:00:00Z</pubdate>
        <category>Technology and Code</category>
        <author>Joris Paret</author><author>Brian Sammuli</author><author>Víctor Costa Pérez</author><author>Nathaniel Saura</author><author>Laura Hernández Cubo</author><author>Daniel Iglesias</author>
        <description><![CDATA[The Tokamak Systems Monitor (TSM) is a software suite under development at ITER that provides operators with an integrated view of the tokamak’s engineering health based on operational instrumentation. A key functionality of TSM is anomaly detection, aimed at identifying unexpected behaviors across a wide range of systems. To this end, a dedicated anomaly detection module is being developed to integrate multiple machine learning-based algorithms, ranging from intershot classification of complete pulses to online detection of localized events. The current status of this module and its roadmap for future development are illustrated with two implemented examples: an intershot algorithm that uses dimensionality reduction and clustering to classify gyrotron pulses, and a time-localized approach based on an invertible neural network to monitor magnet power supplies. Automated warnings generated by the module will support operators in evaluating anomalies, thereby enhancing the reliability of ITER operations.]]></description>
      </item>
      </channel>
    </rss>