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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>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-09-06T01:01:45.431+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1874615</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1874615</link>
        <title><![CDATA[Biomass-based N-TiO2 composites for polyvinylchloride nano plastic photodegradation]]></title>
        <pubdate>2026-09-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Kuljit Kaur</author><author>Harpreet Kaur</author>
        <description><![CDATA[Plastic pollution of the environment and water is a persistent global concern. Microplastic contamination has impacted aquatic environments, demanding the development of efficient remediation techniques to address this issue. In this work, N-TiO2–supported corncob activated (ZnCl2) carbon (CCAC/N-TiO2) composites (CT13) (CT11), and (CT31) were synthesized through a simple wet-impregnation approach. The prepared composite materials were characterized using Fourier transform infrared (FTIR) spectroscopy, XRD (X-ray diffraction), SEM (Scanning electron microscopy), EDS (Electron dispersive spectroscopy), XPS (X-ray photoelectron spectroscopy), thermogravimetric analysis (TGA), Photoluminescence (PL) spectroscopy, UV-visible spectroscopy and Dynamic Light Scattering (DLS) techniques. Tauc’s method was applied to evaluate the optical band gap, and the results indicated that the composite material’s spectral response extended into the visible-light region, accompanied by a significant reduction in band gap energy. The removal performance of the CCAC/N-TiO2 composites toward PVC-NPs was systematically evaluated under different pH conditions (4, 7, and 10), varying contact times, and various light conditions. CT13 composite demonstrated exceptional performance, achieving a 94% degradation efficiency after 180 min of exposure to tungsten light. The removal of PVC-NPs was determined to occur via a photocatalytic pathway and was confirmed by quenching experiments. Additionally, SEM, FTIR, DLS, and fluorescence microscopy verified the presence of PVC-NPs on the composite surfaces under both dark and light conditions. The major photodegradation products were identified using gas chromatography-mass spectrometry (GC-MS). The addition of CCAC to N-TiO2 significantly improved its ability to remove PVC-NPs. This is because the CCAC addition increased the number of active sites for adsorption. The CT13 composite’s surface attracts and captures the PVC-NPs through a variety of interactions, including hydrophobic interactions, electrostatic attractions, π-π interactions, halogen bonding, and hydrogen bonding. This strong adsorption increases the number of available reaction sites, which in turn boosts the photocatalytic removal of the PVC-NPs. This study sheds light on the use of biomass-derived materials for water purification, providing a long-term solution to pollution and agricultural waste issues.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1924829</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1924829</link>
        <title><![CDATA[Capturing full-process creep in cemented backfill: a modified generalized Kelvin model with fractional derivative and hardening function]]></title>
        <pubdate>2026-09-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yiying Feng</author><author>Yiming Wang</author><author>Dongxing Fu</author>
        <description><![CDATA[Understanding time-dependent deformation in cemented backfill is critical for green mining stability. This study proposes a fractional-order creep constitutive model that couples hardening and damage effects across the full creep process. A hardening function is introduced into the deformation modulus, while a Caputo fractional dashpot is integrated into a modified generalized Kelvin framework. This enables the model to capture both decelerating and accelerating creep phases. Numerical simulations show that low stress levels induce hardening-dominated creep with decreasing rates, whereas high stress levels trigger damage-driven acceleration and eventual failure. Validation against experimental data under three stress levels shows excellent agreement. Comparative analysis demonstrates clear advantages over classical fractional Nishihara models in describing accelerated creep. Parameter sensitivity analysis confirms model robustness and clarifies the distinct roles of fractional order and hardening factors. Overall, this work offers a reliable theoretical tool for predicting creep and assessing long-term stability in cemented backfill structures, with meaningful implications for sustainable mining engineering.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1870780</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1870780</link>
        <title><![CDATA[First-principles investigation of YXMnH6 (X = Hg, Zn) complex hydrides: structural, electronic, optical, mechanical, and thermodynamic insights for H2 storage efficiency]]></title>
        <pubdate>2026-08-28T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Doha Shtaiwi</author><author>Mohammed Abu-Jafar</author><author>Asif Hosen</author><author>Hasan Masri</author>
        <description><![CDATA[This paper explores the structural, electronic, mechanical, optical, and thermodynamic characteristics of complex hydrides YXMnH6 (X = Hg, Zn) to be used in hydrogen storage. The investigation is carried out through first principles calculations. Both compounds are dynamically and thermally stable, as indicated by the lack of negative frequencies in phonon dispersion calculations as well as being confirmed by AIMD simulations at room temperature with minimal fluctuations and no structural degradation. Electronic property-based YHgMnH6 and YZnMnH6 compounds exhibit semiconducting behavior, with indirect band gaps of 1.445 eV using PBE-GGA (1.826 eV using mBJ-GGA), and 1.555 eV using PBE-GGA (2.548 eV using mBJ-GGA), respectively. Its structural features indicate that YHgMnH6 is characterized by the largest lattice constant (6.8623 Å) as compared to YZnMnH6 (6.6506 Å), as mercury has a higher atomic radius than zinc. According to the Born stability criterion, both compounds are verified to be mechanically stable. 1t is also shown that YZnMnH6 has a high Young’s modulus and is appropriate for uses requiring hardness and resistance to deformation. Common optical property analysis indicates strong optical response of the ultraviolet region on both compounds; thus, they are good candidates in photovoltaic and optoelectronic applications. The hydrogen storage contents are estimated at 1.73 wt% of YHgMnH6 and 2.81 wt% of YZnMnH6. In general, these findings suggest that YXMnH6 (X = Hg, Zn) complex hydrides are versatile substances and have the potential to be used in hydrogen storage systems and clean technologies. Further research will be in the field of experimental establishment of YXMnH6 (X = Hg, Zn) in order to verify existing results and further study its energy applications.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1937209</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1937209</link>
        <title><![CDATA[Engineering geological characteristics of the working face weathering zone and its impacts on mining effect: a case study of Zhangji coal mine]]></title>
        <pubdate>2026-08-27T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xiaorong Zhai</author><author>Ziqing Zhang</author><author>Kangjian Wang</author><author>Ming Tang</author>
        <description><![CDATA[In concealed coalfields, weathering zones within the roof overburden stratum play a critical role in the excavation of shallow working faces. The weathering process affects the engineering geological properties of the rocks, complicating roof support management and increasing the risk of hydrogeological hazards. To elucidate these effects, the 1410 (3) working face of the Zhangji Coal Mine in the Huainan mining area, Anhui Province, was selected for detailed investigation. Through underground drilling, a comprehensive exploration and coring of the rock stratum within a 30 m interval from the roof of the working face were conducted. Employing X-ray diffraction (XRD), an analysis of the mineral composition of rocks in the weathering zone was conducted. Rock cores obtained from various locations underwent physical and mechanical testing to determine the depth and degree of weathering zones. Two geological models were established: weathering stratum and normal stratum. Numerical simulation was employed to study the mining impact on surrounding rocks under these two different conditions. The results were as follows: (1) Weathering stratum were found to be distributed across the roof area of the working face, with depths ranging from 18 to 32 m and an average depth of 25 m. The weathering rocks exhibited darker coloration and poorer structural integrity compared to normal rocks. (2) The mineral composition of the bedrock weathering zone was predominantly clay minerals, such as kaolinite, montmorillonite, and illite, which were highly susceptible to expansion upon contact with water. This characteristic was beneficial in inhibiting the development of water-conducting fracture zones. (3) The average porosity of normal and weathering rock was 14.23% and 19.92%, respectively, reflecting a 40.02% increase. The average uniaxial compressive strength for normal and weathering stratum was 40.44 MPa and 17.78 MPa, respectively, indicating a 56.03% decrease. The average tensile strength of normal and weathering stratum was 3.69 MPa and 1.21 MPa, respectively, showing a 67.23% decrease. (4) Numerical simulation revealed that the height of the plastic failure zone and vertical displacement on the roof of the working face were significantly elevated due to rock weathering. The weathering effect induced substantial fluctuations in the stress field of the surrounding rocks within the roof range of the goaf, demonstrating a strong disturbance mechanism.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1916063</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1916063</link>
        <title><![CDATA[A security analysis scheme for physical layer chaos encryption based on neural networks]]></title>
        <pubdate>2026-08-26T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Dongfei Wang</author><author>Jiaying Wu</author><author>Jiaao Wang</author>
        <description><![CDATA[IntroductionPhysical layer encryption schemes hold significant importance for contemporary data protection due to their flexible operations, comprehensive data protection capabilities, and low-cost, fast computation. However, there is currently no universal and effective method for security analysis of physical layer encryption systems. With various physical layer encryption methods emerging, a critical issue is whether the designed systems can effectively resist unauthorized attacks.MethodsThis paper enhances the security evaluation of physical layer encryption schemes from the perspective of cryptanalysis, moving beyond simply using the size of the key space to evaluate the security of algorithms. The computational security of physical layer chaos encryption schemes is investigated by analyzing their ability to resist attacks. A neural network-based attack scheme against physical layer chaos encryption is proposed.ResultsComputer simulations verify the feasibility and effectiveness of the attack. Experimental results demonstrate that the proposed method can learn effective ciphertext-to-plaintext mappings under the tested fixed-key conditions. The method involves a moderate offline training cost and achieves sub-millisecond model-level inference after training.DiscussionThe results demonstrate that the investigated physical layer chaos encryption schemes are vulnerable to neural network-based attacks under the tested conditions. The proposed method provides a complementary security-evaluation approach for the investigated Arnold- and Chen-based encryption schemes.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1892087</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1892087</link>
        <title><![CDATA[Bifurcation between pre- and post-nucleation models in fluorapatite Liesegang patterns]]></title>
        <pubdate>2026-08-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Kotoha Harima</author><author>Takuto Takeda</author><author>Hideki Nabika</author>
        <description><![CDATA[Liesegang patterns, characterized by periodic precipitation, typically follow either a pre-nucleation or post-nucleation model. However, the factors governing the selection of either pathway in a single chemical system are poorly understood. In this study, we demonstrated a concentration-driven bifurcation between the pre-nucleation and post-nucleation models during the formation of fluorapatite (FAp) Liesegang patterns in an agarose matrix using two systematic sets of experimental conditions. First, a three-variable matrix systematically varying the concentrations of the three involved electrolytes (i.e., calcium chloride, phosphate buffer, and sodium fluoride) revealed a distinct difference between the pre- and post-nucleation pathways. Using time-lapse imaging, line profile analysis, and X-ray diffraction, we monitored the evolution of amorphous calcium phosphate (ACP) intermediates into other species such as crystalline FAp. At certain concentration conditions, the system followed a pre-nucleation model, in which discontinuous ACP growth and subsequent crystallization-induced shrinkage produced discrete bands. By contrast, other conditions triggered a post-nucleation pathway, in which a spatially continuous ACP phase first precipitated and then separated into discrete bands upon structural conversion. Second, by fixing the outer electrolyte (calcium chloride) concentration and systematically varying the two inner electrolyte concentrations (phosphate buffer and sodium fluoride), we successfully mapped the continuous crossover between these two regimes and identified a transition boundary where the features of both pathways co-existed. The study proposes that the bifurcation between the two models would be governed by the kinetic balance between the generation of metastable ACP and its structural conversion. These findings provide a broadly applicable framework for understanding pattern formation in mineral systems, which progress through metastable intermediates.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1926250</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1926250</link>
        <title><![CDATA[Enhanced erosion resistance of seawater-based bio-cemented sand via soybean hull-derived EICP]]></title>
        <pubdate>2026-08-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xin Hu</author><author>Gaoqun Chu</author><author>Huiming Tan</author>
        <description><![CDATA[Coastal erosion severely threatens the stability of sandy shorelines, necessitating low-cost and sustainable bio-mediated treatment methods for shoreline protection. This study investigates an enzyme-induced carbonate precipitation (EICP) approach utilizing crude urease extracted from soybean hulls and concentrated seawater as an ionic source to enhance the erosion resistance of sand. The joint effects of urease activity, solution volume, treatment cycles, seawater concentration, and curing time were systematically evaluated through penetration strength, wind erosion, and hydraulic erosion tests, complemented by carbonate content measurements. Testing results indicate that the urease activity of the soybean hull extract scaled with the dosage, reaching a maximum of 2.27 mmol L-1·min-1. The seawater-based EICP treatment successfully formed a hardened surface crust, increasing the surface penetration strength to 0.841 MPa, while the critical wind velocity and critical flow velocity increased by up to 7.2 and 6.4 times, respectively, compared with untreated sand. Notably, multi-cycle spraying proved superior to a single application with the same total solution volume. While urease activity was the dominant factor governing carbonate production under relatively low-activity conditions, the number of treatment cycles exerted the strongest influence on hydraulic erosion resistance. Furthermore, power-law relationships were established between erosion-resistance indices and both carbonate content and penetration strength. Carbonate content showed consistently stronger correlations with erosion resistance than penetration strength, indicating that it provides a practical and reliable indicator for evaluating the erosion resistance of seawater-based EICP-treated sand. These findings demonstrate the feasibility of coupling agricultural by-product-derived urease with seawater-based EICP for cost-effective and sustainable stabilization of coastal sandy soils.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1896122</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1896122</link>
        <title><![CDATA[Reinforcement learning-driven adaptive rewiring modulates fragmentation depth in bounded-confidence opinion dynamics]]></title>
        <pubdate>2026-08-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Quang Nguyen</author><author>Nha Binh Truong</author>
        <description><![CDATA[The structure of an interaction network strongly shapes opinion clustering and the emergence of echo chambers in bounded-confidence (BC) models. We ask whether a controller can steer this clustering by rewiring edges adaptively and how a learned policy compares with hand-designed heuristics. We train a dueling double deep Q-network (DQN) with a candidate-aware state to select degree-preserving edge swaps in a Deffuant model on a sparse Erdős–Rényi (ER) graph (N=500, ⟨k⟩=7.8). Augmenting the state with the signed discord change of each candidate is essential: without it, the agent fails to learn. With it, training reward saturates within ∼200 episodes. Pooling over 10 independent graph realizations and 25 opinion initializations per graph (250 trials per strategy), the learned heterophilic policy reaches a median cluster count C=7.0 at ε=0.18, significantly above the no-rewiring baseline (C=3.0, p<10−3) and the greedy heterophilic oracle (C=6.0). The RL benefit is mode-asymmetric: reinforcement learning (RL) exceeds greedy heterophilic rewiring in 9/10 graphs (Cliff’s δ=+0.26, small effect) but is exceeded by greedy homophilic rewiring in 9/10 graphs (Cliff’s δ=−0.21, small effect). A sweep over ε∈[0.12,0.30] reveals a sigmoidal nucleation barrier for reaching C≥6, with directed strategies giving up to 2.5× speedup over random rewiring. Interpretation: at intermediate ε, the natural attractor is bipolar, so homophilic and heterophilic rewiring amplifies fragmentation; what they control is the depth, not the sign. This finding delimits what rewiring-based interventions can and cannot achieve within the abstract BC framework and identifies the regimes where multistep planning outperforms myopic heuristics.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1927967</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1927967</link>
        <title><![CDATA[A side-channel attack for recovering keys in HMAC-SM3 algorithm]]></title>
        <pubdate>2026-08-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zhen Wu</author><author>Zhiguang Qin</author><author>Kai Wang</author>
        <description><![CDATA[Cyber-Physical-Social Systems (CPSS) face escalating side-channel threats that undermine secure data transmission and authentication. As China’s national cryptographic hash standard, SM3 is widely deployed in CPSS-integrated social network ecosystems for identity authentication, API signing, and cross-platform data integrity verification—yet its key-dependent input vulnerabilities against side-channel attacks remain inadequately addressed. This study tackles two critical limitations of traditional side-channel attacks for HMAC-SM3 key recovery: non-profiling methods fail due to absent plaintext correlations, while profiling-based approaches suffer from error accumulation and near-zero success rates in single-trace scenarios. We propose a self-calibrating side-channel attack (SC-SCA) that enables high-accuracy HMAC-SM3 key recovery using only a single power trace during the attack phase. The method constructs a Bayesian network to integrate power trace statistics with prior knowledge of input dependencies, then performs joint probabilistic inference via belief propagation. Experimental results demonstrate 100% key recovery success under simulated noiseless conditions, 91.45% success on a real smart card system, and 73% effectiveness at a 10 dB signal-to-noise ratio. Crucially, this work exposes a previously overlooked attack surface in CPSS-based social networks: a single compromised HMAC-SM3 key can enable forged device control commands, large-scale privacy breaches, and cascading identity theft across linked social platforms. Our findings provide both a practical security benchmark for CPSS edge devices and theoretical foundations for designing side-channel-resistant cryptographic implementations.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1857070</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1857070</link>
        <title><![CDATA[Interdisciplinarity as a centripetal force: physics-based methods for complex systems to artificial intelligence]]></title>
        <pubdate>2026-08-14T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Giovanna Zimatore</author><author>Piercesare Grimaldi</author><author>Stavros Hatzopoulos</author><author>Piotr Henryk Skarzynski</author>
        <description><![CDATA[Analytical tools derived from nonlinear dynamics and dynamical systems theory, such as phase-space reconstruction and Recurrence Quantification Analysis (RQA), provide a powerful framework for investigating complex systems across different scientific domains. These methods allow the identification of dynamical structures, including recurrence, nonlinearity, and transitions between states, in time series data originating from diverse contexts. Scientific research is often shaped by two opposing forces that resemble the dynamics of physics: a centrifugal force, associated with increasing specialization, and a centripetal force, associated with interdisciplinarity. The rapid development of technologies and analytical methods has led to highly specialized languages and frameworks, which, while enabling scientific progress, can also generate fragmentation and communication barriers between disciplines. In contrast, interdisciplinarity emerges as a centripetal force that promotes the identification of shared analytical frameworks across domains. In this context, the transfer of methods is not merely a consequence of mathematical convenience but reflects the presence of common dynamical properties governed by similar physical principles. Artificial intelligence, integrated within physics-informed computational frameworks, provides a powerful tool for analyzing complex, high-dimensional, and heterogeneous datasets while preserving the dynamical structure of the underlying system. This convergence is not merely technical: the same nonlinear dynamical principles that govern physiological and cognitive systems appear to operate within artificial ones, suggesting that AI is not external to the phenomena this manuscript addresses but continuous with them. This inherent interdisciplinarity positions AI as a centripetal force, drawing together methods, languages, and findings from otherwise distant disciplines around a shared dynamical core.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1897232</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1897232</link>
        <title><![CDATA[Nonlinear responses of station-area functional vitality to the built environment in urban rail transit networks: an interpretable machine learning approach]]></title>
        <pubdate>2026-08-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Weixin Zhu</author><author>Zhengyang Fang</author>
        <description><![CDATA[Functional vitality in urban rail transit station areas reflects the concentration of services and urban functions around stations. This study examines 170 operational stations on Lines 1–8 of the Ningbo rail transit network. Using point-of-interest (POI), bus stop, street network, station attribute, and rail network data within 800 m catchments, we constructed a station-area functional vitality index and investigated its built-environment determinants using XGBoost, SHapley Additive exPlanations (SHAP), and partial dependence plots (PDPs). XGBoost outperformed the comparison models, with an R2 value of 0.7213, a root mean square error (RMSE) of 0.0801, and a mean absolute error (MAE) of 0.0589. Distance to the city center, intersection density, and the number of bus stops were the dominant predictors, together accounting for 71.55% of total importance. One-dimensional PDPs revealed marked nonlinear responses: predicted vitality increased at approximately 105–113 intersections/km2 and around 16–17 bus stops but declined rapidly as distance from the city center increased within the first 4 km. Two-dimensional PDPs further suggested that central location conditions the effects of other built-environment factors, while strong street connectivity combined with sufficient bus stop provision corresponds to higher predicted vitality. These findings provide evidence for station-area functional planning and bus–rail integration.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1817906</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1817906</link>
        <title><![CDATA[Design and characterization of high-efficiency 2D-material-based MOS-HEMTs for next-generation sustainable electronics]]></title>
        <pubdate>2026-08-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author> Neelu</author><author>Kaushik Mazumdar</author>
        <description><![CDATA[The noteworthy, astonishing, and remarkable electronic properties of graphene make it a reliable material for the fabrication of downscaled and high-frequency devices in the next and future generations. In this research work, the design approach of a graphene-based metal-oxide semiconductor high-electron-mobility transistor (MOS-HEMT) using the Silvaco ATLAS simulation tool is demonstrated, in which undoped graphene is used as the channel material, and graphene oxide (GO) functions as the oxide layer. It is investigated that at a particular threshold voltage VT = 1 V, the value of drain current (Id) is 1,280 mA, which demonstrates a twofold enhancement compared to GaN-based MOS-HEMTs. Several appealing characteristics, such as transconductance variation vs. change in gate-to-source voltage (Vgs), carrier mobility variation with different operating temperatures, and device transfer characteristics, are also explored. Accordingly, the obtained and accomplished technical evaluation and assessment of the outcomes validate that graphene could be most suitable for designing next-generation power electronics devices.]]></description>
      </item><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>
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        <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>
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        <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.2026.1833425</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.2026.1835575</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>
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