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        <title>Frontiers in Physics | Interdisciplinary Physics section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/physics/sections/interdisciplinary-physics</link>
        <description>RSS Feed for Interdisciplinary Physics section in the Frontiers in Physics journal | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-07-29T08:01:05.475+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1809931</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1809931</link>
        <title><![CDATA[Assessing pegmatite compositional evolution through analysis of muscovite from historic North Carolina mining districts by handheld laser-induced breakdown spectroscopy (hLIBS)]]></title>
        <pubdate>2026-07-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Russell S. Harmon</author><author>Michael A. Wise</author><author>Robert M. Ramos</author><author>Adam C. Curry</author>
        <description><![CDATA[Mineral exploration provides the commodities society requires, but continuing discovery and development of new critical mineral deposits is necessary to realize a sustainable, low-carbon future. Pegmatites in historic mining districts across North Carolina have been sites of mineral production for over a century, initially for mica and subsequently for spodumene. Most granitic pegmatites here are mineralogically simple feldspar, quartz, and muscovite pegmatites, with beryl, spodumene, and columbite-group minerals present in some chemically complex pegmatites. Analysis for Li, K, Zn, Ga, Rb, Sn and Cs content and K/Rb ratio by handheld LIBS has been undertaken for suites of muscovite samples from pegmatites in five historic mining districts across North Carolina—the Spruce Pine, Franklin-Sylva, and Cashiers districts in the Blue Ridge province and the Shelby district and Carolina Tin-Spodumene Belt (CTSB) in the Piedmont province, to assess their petrological character, degree of compositional evolution, and potential for rare element mineralization. Most pegmatites have only undergone moderate degrees of melt differentiation and compositional fractionation, and only rarely has melt evolution reached the extreme extent required for beryl or spodumene formation. The quartz-feldspar pegmatites of the Franklin-Sylva, Cashiers, and Shelby districts are poorly to moderately fractionated, whereas moderately to highly fractionated spodumene-bearing pegmatites are present in both the Spruce Pine district and Carolina Tin-Spodumene Belt. Muscovite from spodumene-bearing pegmatites is not unusually enriched in Li, but can be distinguished from its counterpart in common quartz-feldspar pegmatites by characteristically low K/Rb ratios of <40 and Li contents exceeding 0.05 wt. %.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1855044</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1855044</link>
        <title><![CDATA[Security prediction and reliability analysis method of smart grid based on edge computing]]></title>
        <pubdate>2026-07-27T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Qiang Li</author><author>Shizeng Liu</author><author>Jinyun Yu</author><author>Tongbo He</author><author>Chunhai Guo</author><author>Yunhai Song</author><author>Shihong Zhang</author><author>Yi Luo</author>
        <description><![CDATA[In order to solve the problem of high latency and low precision collaborative optimization in smart grid security prediction, this paper proposes a smart grid security prediction and reliability analysis method (EA-LSTM-GraphAgg, EALG) based on edge computing. In the initial prediction stage, EA-LSTM is deployed on edge servers in various regions. Through collaborative optimization of attention mechanism and competitive random search, the temporal traffic data of smart grid collection nodes is locally processed to generate local initial prediction results. In the aggregation stage, to address the local limitations of initial predictions, a GraphAgg aggregation network is designed. The EA-LSTM prediction results are transmitted through lightweight collaborative directional transmission between edge nodes, rather than the original data. At the edge, a graph structure is constructed with smart grid devices as nodes and communication connections as edges. Through localized information aggregation, node traffic associations are mined to improve prediction accuracy. Experiments and analysis show that EALG can output high-precision smart grid security prediction results. It can provide reliable support for smart grid reliability analysis and traffic anomaly warning in smart grid operation and maintenance.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1863258</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1863258</link>
        <title><![CDATA[Physical representation and cross scale dynamics modeling of digital trade network structure]]></title>
        <pubdate>2026-07-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Huihong Liu</author><author>Jin Huang</author><author>Lixi Zhao</author><author>Shuoguo Zhao</author>
        <description><![CDATA[IntroductionThis study introduces an innovative framework for modeling the physical representation and cross scale dynamics of digital trade network structures, addressing limitations in traditional approaches that often fail to capture the hierarchical and semantic complexities inherent in such systems.MethodsThe proposed methodology is organized into three core components: preliminaries, the HierarchicalSemanticOptimal model, and a modular structure learning strategy. The preliminaries establish the theoretical foundation by formalizing the problem and defining key mathematical constructs necessary for subsequent analysis. The HierarchicalSemanticOptimal model is designed to encapsulate the multifaceted dynamics of digital trade networks through a hierarchical composition mapper, a semantic transition checker, and an optimal transport matcher, enabling a nuanced understanding of trade flows and resource distribution. The modular structure learning strategy further enhances the model’s adaptability and scalability by employing rule guided inference mechanisms to dynamically adjust both parameters and structural configurations in response to evolving network conditions.Results and DiscussionExperimental evaluations demonstrate the efficacy of the proposed framework, revealing significant improvements in modeling accuracy and computational efficiency compared to existing methods. These findings underscore the potential of the framework to provide deeper insights into the optimization of digital trade networks, facilitating more efficient trade operations and resource allocation across diverse scales and contexts.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1773784</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1773784</link>
        <title><![CDATA[Dual-driven physics–data and signal perception synergy: a layered fault diagnosis framework for hybrid islanded power supply systems]]></title>
        <pubdate>2026-07-22T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Qiang Bian</author><author>Weibo Li</author><author>Yi Wang</author><author>Yi Zhou</author><author>Hualiang Fang</author>
        <description><![CDATA[In an isolated island environment, a hybrid power supply system of fuel, biomass, wind, solar, and storage faces multiple challenges, such as multi-source heterogeneity, strong coupling of AC and DC, and resource constraints. Its fault characteristics show cross-domain propagation concealment and multi-scale dynamic complexity. Traditional single diagnostic paradigms have difficulty balancing real-time performance, accuracy, and interpretability. Therefore, this article proposes a physics- and data-driven hierarchical fault diagnosis framework that is coordinated by signal perception. This framework builds a three-level collaborative mechanism of “end-edge-cloud:” at the device perception layer, it uses signal processing techniques such as wavelet packet transform combined with lightweight mechanism rules to achieve millisecond-level locking of hard faults in power electronic devices and transient protection. At the regional decoupling layer, it integrates the data mining capabilities of graph neural networks (GNNs) with the power grid topology model to accurately analyze the cross-domain propagation paths of faults in AC and DC hybrid systems. At the system decision-making layer, it establishes a “physics–data dual-drive” closed loop, using long short-term memory networks (LSTMs) to capture nonlinear dynamic residuals to correct physical models, and at the same time introduces physics-informed neural networks (PINNs) to constrain the training boundaries of data-driven models, ensuring that decisions comply with physical laws such as energy conservation. In addition, for the fluctuation of wind and solar power and computing power constraints in isolated island scenarios, a feature adaptive strategy driven by transfer learning and a model pruning algorithm are designed. Verification of typical cases, such as gas turbine surge warning and energy storage converter cascading fault analysis, shows that this framework can effectively break through the weak fault detection bottleneck in a strong noise background, significantly reducing the false alarm rate while ensuring a high detection rate. This framework provides a new paradigm for improving the overall resilience and intelligent operation and maintenance level of isolated island hybrid power supply systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1878336</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1878336</link>
        <title><![CDATA[Network-position heterogeneity and nonlinear risk propagation in supply chain networks under external shocks]]></title>
        <pubdate>2026-07-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Qiaoming Hou</author><author>Jinrong Ma</author>
        <description><![CDATA[Amid the increasing complexity and uncertainty of global supply chains, external shocks have become a major source of systemic risk in supply chain networks. Existing studies are largely based on homogeneous assumptions and single-system frameworks, making it difficult to reveal the coupling interactions among firms with different network positions and their effects on the dynamic evolution of risk propagation. To address this gap, this study is positioned as a theoretical modeling and numerical simulation study. From the perspective of network-position heterogeneity among supply chain firms, it integrates propagation dynamics with discrete dynamics to construct a heterogeneous, dual-population coupled spatiotemporal model of risk propagation. The model is used to investigate the nonlinear evolution mechanisms of shock-driven risk propagation in supply chains. The results show that: (1) The basic reproduction capacity of systemic risk is jointly determined by the risk propagation reproduction capacities of high-network-position firms and ordinary firms. In addition, the stronger the business coupling with high-network-position firms, the more pronounced the network risk propagation effect.(2) When the number of core firms is relatively large and the business dependence between core firms and ordinary firms is strong, local risks are more likely to spread through supply chain relationships and evolve into systemic risks. The more concentrated the initially infected firms are, the more likely risks are to form spatial clusters in local areas and further diffuse to surrounding firms.(3) The timing of intervention plays a decisive role in risk control effectiveness. The optimal control strategy constructed in this study can suppress risk propagation in the network at the early stage and exhibits stronger risk suppression capability in high-risk network structures characterized by core-firm agglomeration and close business connections. However, when the initial risk sources are highly concentrated, risks possess stronger initial diffusion potential, which substantially increases the difficulty of risk control. This study reveals the spatiotemporal evolution mechanism of supply chain risk propagation driven by network-position heterogeneity and coupling effects, enriches the dynamical theory of systemic risk propagation in complex networks, and provides a new theoretical perspective for studying risk propagation and system stability in heterogeneous complex systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1828648</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1828648</link>
        <title><![CDATA[A security monitoring and warning method for economic growth and unemployment in financial social networks]]></title>
        <pubdate>2026-07-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Lei Liu</author><author>Yuanyuan Wen</author>
        <description><![CDATA[Accurately predicting economic growth and unemployment rate is an important prerequisite for macroeconomic regulation in financial social networks. Traditional security monitoring and warning methods often rely on lagging official statistical data, making it difficult to capture nonlinear correlations and sudden signals in economic dynamics in real time. Therefore, this paper proposes a Boruta-SHAP and Transformer-XL (BST-XL) security monitoring and warning model based on Transformer-XL. The framework first constructs a Boruta-SHAP (BS) two-stage feature selection method based on ensemble learning framework and Shapley Additive Explanations (SHAP) interpretability analysis. By identifying important features related to economic time series task from a given feature set, focusing on economic growth and unemployment rate, the problem of feature redundancy in economic time series data is effectively solved. Secondly, by using Transformer-XL to measure the impact of historical economic sequences on recent economic dynamics, the latest state characteristics of economic dynamics can be obtained. This is beneficial for accurately predicting the dynamic changes of economic growth and unemployment rates. Experimental analysis shows that BST-XL performs well in predicting economic growth and unemployment rate, with higher security monitoring accuracy. It is suitable as a predictive model for economic growth and unemployment rate in financial social networks.]]></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.2026.1833425</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.2026.1841183</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.2026.1829651</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.2026.1872936</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.2026.1824409</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.2026.1832129</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1832129</link>
        <title><![CDATA[Effects of space weather on the electric power network and mining operations in Alberta, Canada during the October 10–11, 2024 geomagnetic storm]]></title>
        <pubdate>2026-05-28T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Darcy Cordell</author><author>Hannah Parry</author><author>Ryan MacMullin</author><author>Sherry Gao</author><author>Mahendra AC</author><author>Martin Connors</author><author>Sunil Desai</author><author>Ron Holland</author><author>Ian R. Mann</author>
        <description><![CDATA[Geomagnetically induced currents (GICs) due to space weather can impact electric power networks via transformer saturation, reactive power consumption, and increased total harmonic distortion. In the worst case, these can lead to widespread outages and damage to transformers. Investigating large geomagnetic storm events allows the power industry to better understand and mitigate the associated risks. Here we focus on the impacts in Alberta, Canada during the October 10–12, 2024 geomagnetic storm by modelling GICs in the ≥240 kV power network validated with transformer neutral-to-ground (TNG) GIC measurements. Despite the October 2024 storm being smaller than the May 2024 storm according to global storm disturbance indices, measured GIC exceeded 25 A/phase at three transformers in central Alberta and modelling suggests that the TNG GIC at some transformers in northeastern Alberta exceeded 30 A/phase, similar to the magnitude of the GICs seen during the May 2024 storm. For the October 2024 event, the cause of the largest geoelectric fields and largest GICs is spatially and temporally variable. Large events in northeastern Alberta were linked to the sudden storm commencement, with some large dusk-side events in southern Alberta being linked to a low-latitude substorm. The most spatially-extensive large geoelectric field event was associated with a large nightside substorm which developed over Alberta. In addition to GICs flowing at high voltage levels, the October 2024 storm also resulted in adverse technological impacts in northeastern Alberta at lower voltage levels. These included 34.5 kV capacitor banks and large 13.8 kV mine shovels tripping offline at industrial mining operations. The capacitor bank trips were likely due to increased total harmonic distortion due to transformer saturation from GIC at higher voltage levels, while the mine shovel trips may have been due to a unique failure mode related to induced current on long (∼10 km) low-current 30 V DC ground check circuits. To our knowledge, this is the first time that direct space weather impacts on mining operations have been reported in the literature.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1817865</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1817865</link>
        <title><![CDATA[Joint security and energy optimization in UAV-enabled smart grid networks]]></title>
        <pubdate>2026-05-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jian Wu</author><author>Xiaowei Hao</author><author>Junwei Ma</author><author>Yang Li</author>
        <description><![CDATA[IntroductionRecent years have witnessed an increasing number of Internet of Things devices (IoTDs) deployed in power grids to monitor bidirectional information and power transfer, transforming them into smart grids. The densification of IoTDs in smart grids demands communication solutions that are simultaneously secure against eavesdropping and energy-efficient for sustainable operation.MethodsThis article proposes an unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS)-assisted framework in smart grids that maximizes worst-case secrecy energy efficiency via joint optimization of the UAV’s trajectory, beamforming, and phase shifts of RIS. A twin attention-driven deep reinforcement learning algorithm, TAMRRTD3, is developed, featuring attention-based state representation and regret-aware reward design to enhance learning accuracy and convergence.Results and DiscussionSimulation results indicate that the proposed algorithm achieves a faster convergence rate and enhanced secrecy energy efficiency than the benchmark algorithms.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1779002</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1779002</link>
        <title><![CDATA[Physics-informed neural network framework for fast and accurate nanofluid heat-transfer prediction in microchannel heat sinks]]></title>
        <pubdate>2026-04-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Hend Khalid Alkahtani</author><author>Galiya Ybytayeva</author><author>Ayman Qahmash</author>
        <description><![CDATA[IntroductionPhysics-informed, data-efficient surrogates for microchannel cooling are needed as power densities in electronics and photonics approach thermal limits, yet design workflows still rely on empirical correlations or computationally expensive simulations. This study aims to provide a fast, interpretable, physically consistent, and reliable predictor for heat transfer coefficient, pressure drop, and hotspot temperature in nanofluid microchannels.MethodsWe developed a hybrid physics-informed neural network trained on more than 10,000 tabulated records curated from a Kaggle repository and treated as compiled heterogeneous operating condition response pairs with limited case-level traceability. The framework uses physics-constrained inputs derived from nanofluid mixture rules and slip-related descriptors, and optimizes a composite loss combining data mismatch, reduced-order conservation-residual penalties, and boundary-condition inconsistency penalties in operating-condition space. To enrich sparse regions, physics-consistent synthetic augmentation was applied by sampling admissible conditions and retaining only feasible samples that satisfied constraint checks.ResultsThe proposed model outperformed strong baselines, achieving a mean absolute error of 11.3, a root mean squared error of 18.4, and a coefficient of determination of 0.96 on the test set. Physics-consistency auditing showed 98.7% feasibility with low residual magnitudes and low boundary-violation rates. Inference profiling yielded approximately 15.7 ms on a GPU and 120 ms on a CPU for single-sample evaluation.DiscussionThe results support the use of physics-informed learning as an auditable surrogate for rapid design screening in nanofluid microchannel applications. The framework reduces reliance on repeated high-fidelity simulation while reporting residual and boundary metrics alongside predictive accuracy. However, controlled hardware testbed validation is still required before industrial deployment.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1808725</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1808725</link>
        <title><![CDATA[Commentary: Astrophysical constraints on the simulation hypothesis for this Universe: why it is (nearly) impossible that we live in a simulation]]></title>
        <pubdate>2026-04-21T00:00:00Z</pubdate>
        <category>General Commentary</category>
        <author>Eliott Edge</author><author>Chad Ashton Brown</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1667538</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1667538</link>
        <title><![CDATA[The Hofstadter butterfly: bridging condensed matter, topology, and number theory]]></title>
        <pubdate>2026-04-14T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Indubala I. Satija</author>
        <description><![CDATA[Celebrating its golden jubilee, the Hofstadter butterfly fractal is a remarkable fusion of art and science. This iconic “X”-shaped fractal captivates physicists, mathematicians, and enthusiasts alike by elegantly illustrating the energy spectrum of electrons within a two-dimensional crystal lattice influenced by a magnetic field. Enriched with integers of topological origin that serve as quanta of Hall conductivity, this fractal and its variations have become paradigm models for topological insulators, which are novel states of matter in twenty-first-century physics. This article delves into the theoretical framework underlying butterfly fractality through the lenses of geometry and number theory. Within this poetic form of mathematics, we witness a rare form of quantum magic: the appearance of abstract fractals in the construction of the butterfly graph itself. In its simplest form, the butterfly graph tessellates a two-dimensional plane with trapezoids and triangles, where the quanta of Hall conductivity are embedded in the integer-sloped diagonals of the trapezoids. The theoretical framework is succinctly expressed through unimodular matrices with integer coefficients, bringing to life abstract constructs such as the Farey tree, the Apollonian gaskets, and the Pythagorean triple tree.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1670851</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1670851</link>
        <title><![CDATA[Study on scale effect of ship on flow field and near-field wave generation]]></title>
        <pubdate>2026-04-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yu-Hong Chen</author>
        <description><![CDATA[Ship resistance is one of the most important factors for ship design. Due to the inability to satisfy full similarity conditions, the scale effect between model and full-scale ships has garnered significant attention and become a major focus of the ITTC. Traditionally, studies on scale effects have often focused on resistance, while little research can be found about its underlying flow mechanisms and influence on near-field wave generation. Therefore, this paper investigates the flow characteristics around the KCS hull of different scales (scale ratios α = 1.0, 4.0, 31.6, 60.0) under Fr similarity conditions by CFD method using STAR CCM+. The near-field wave generation is analyzed in detail, with a focus on wave profiles around the hull, free surface wave elevation and wave cut, wake flow field, and the distribution of the pressure field. The study reveals the differences in near-field flow characteristics caused by scale-induced Re non-similarity and their impact on near-field wave generation. This research helps to further elucidate the influence of scale effects on the flow field, thus offering valuable insights for improving the accuracy of full-scale ship resistance predictions, leading to a reduction in full-scale resistance prediction error by up to 17.56% compared to traditional ITTC model-scale extrapolation methods (e.g., the Prohaska-based approach).]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1834152</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1834152</link>
        <title><![CDATA[Editorial: Mathematical physics methods and advanced materials in Frontier applications for underground engineering]]></title>
        <pubdate>2026-04-09T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Yiying Feng</author><author>Jiangyu Wu</author><author>Weiqiang Chen</author><author>Yiming Wang</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1782845</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1782845</link>
        <title><![CDATA[Hyper-S2IR: a model for characterizing higher-order interactions and dynamics in public opinion dissemination]]></title>
        <pubdate>2026-03-26T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Chunying Zhang</author><author>Xiangyu Li</author><author>Lu Liu</author><author>Jing Ren</author><author>Jiang Ma</author><author>Liyan Zhang</author>
        <description><![CDATA[Traditional public opinion diffusion models generally assume interactions between individuals as binary pair-wise effects, which struggle to capture the higher-order complexities of multi-group interactions in social networks—such as group discussions in WeChat and topic reposting on Weibo. Moreover, these models fail to adequately depict the nonlinear trust accumulation mechanisms and individual heterogeneity inherent in the diffusion process. Therefore, the paper proposes a hypergraph-based Hyper-S2IR model for disseminating public opinion. The “Goebbels effect” is operationalized by leveraging the hypergraph structure: a susceptible node’s risk of infection is proportional to its hyperdegree, mathematically representing the cumulative exposure to information from multiple sources within different hyperedges. Our model introduces two types of communicators (HI1 and HI2) with different motivations and capabilities, thereby systematically depicting the inherent heterogeneity of the communication group. Through theoretical derivation, we derive a novel basic reproduction number R0 that explicitly incorporates the hyperdegree distribution of the hypergraph. This R0 provides a threshold for dissemination dynamics: When R0 > 1, the public opinion will continue to spread and converge to a stable public opinion prevalence equilibrium point; when R0 < 1, the public opinion will gradually disappear. Critically, the expression for R0 reveals how higher-order group interactions, encoded in the hyperdegree, fundamentally alter the spreading threshold compared to traditional pairwise networks. Numerical simulations verify the theoretical conclusions and demonstrate that the hypergraph structure significantly accelerates the spread and expands the scale of public opinion compared to traditional network structures. This work provides theoretical support and a quantitative basis for analyzing public opinion dissemination mechanisms and formulating intervention strategies.]]></description>
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