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        <title>Frontiers in Physics | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/physics</link>
        <description>RSS Feed for Frontiers in Physics | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-07-15T05:10:47.248+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1884614</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1884614</link>
        <title><![CDATA[The form factor expansion in the precision β decay era]]></title>
        <pubdate>2026-07-15T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Leendert Hayen</author>
        <description><![CDATA[Precision tests of the Standard Model using β decay have always relied on a careful choice of transition to minimize residual nuclear structure uncertainties. Following breakthroughs in nucleon-level radiative corrections in the last decade, however, corrections due to nuclear structure are once more a limiting factor in several scenarios. Progress in ab initio nuclear theory provides a path forward, but common recoil-order approximations in traditional formalisms often go unnoticed. Here, we critically examine their origin and address recently resolved issues as well as identify open questions.]]></description>
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        <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.2026.1870382</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1870382</link>
        <title><![CDATA[Network properties and information entropy of fractal Koch network via degree-based topological indices]]></title>
        <pubdate>2026-07-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xiu-Jian Wang</author><author>Xiaohong Dong</author><author>Jiadong Si</author>
        <description><![CDATA[The Koch network, as a typical fractal network, exhibits unique topological characteristics and serves as an important model in complex network studies. This study aims to systematically analyze degree-based topological indices in the Koch network to uncover the deeper connections between its structure and informational properties. The analytical expressions for these indices in the Koch network are derived through theoretical analysis and numerical simulations, and the trends of each index with varying network iterations are thoroughly examined. To further explore structural properties, entropy calculations are incorporated to analyze the changes in entropy with vertex degree and network iterations, illustrating entropy trends at different stages. Results indicate that, under the same iteration count t, various entropy values exhibit notable differences, though the overall trends remain consistent. As the iteration count t increases, the topological complexity of the Koch network decreases, with entropy gradually diminishing and reaching its maximum at t=1. With increased iterations, the relative differences among entropy values also progressively narrow. This study provides a theoretical basis for research on Koch network topological indices in chemical graph theory and supports studies on entropy in fractal-based complex networks.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1884610</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1884610</link>
        <title><![CDATA[300 GHz digital holography imaging based on a silver/polypropylene hollow terahertz waveguide]]></title>
        <pubdate>2026-07-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yaya Zhang</author><author>Binzhen Zhang</author><author>Junping Duan</author><author>Lei Cheng</author>
        <description><![CDATA[The major challenge limiting the application of terahertz (THz) imaging quality lies in the significant attenuation of THz waves during free-space transmission. This attenuation arises primarily from water vapor absorption and gas molecule scattering. Compared with free space propagation, low-loss and stable transmission of THz wave can be achieved through the waveguide. Waveguide transmission at low THz frequencies has attracted considerable attention, particularly at around 300 GHz (0.3 THz). Among the various types of THz waveguides, hollow waveguides offer a simple structure, ease of fabrication, low cost, and excellent transmission performance in the THz regime. Here we present a low-loss THz metal dielectric hollow waveguide based on polypropylene (PP) tubing, where an external silver film coated on the PP tube forms a leaky-type hollow waveguide structure. The linear transmission loss is measured to be 1.35 dB/m at 300 GHz. By optimizing this low-loss THz hollow waveguide, we achieve a far-field THz digital holographic (TDH) imaging recording configuration for the first time. To evaluate the imaging performance, different types of samples are measured. Experimental results for a plastic plate with aluminum strips validate a lateral resolution of ∼2.5 mm. The proposed method holds potential as a powerful tool for investigating spontaneous phenomena in the THz band.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1797094</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1797094</link>
        <title><![CDATA[Japanese Traditional Kampo Medicine as a holistic countermeasure for spaceflight-induced physiological and psychological health challenges: a comprehensive review]]></title>
        <pubdate>2026-07-10T00:00:00Z</pubdate>
        <category>Hypothesis and Theory</category>
        <author>Shin Takayama</author><author>Silke Cameron</author><author>Kenny Kuchta</author><author>Tadashi Ishii</author><author>Masahiro Terada</author>
        <description><![CDATA[Spaceflight introduces a complex array of health challenges that affect nearly every physiological system, primarily owing to microgravity, cosmic radiation, isolation, altered environments, and logistical supply constraints. Astronauts commonly experience musculoskeletal degradation, including bone density and muscle mass loss; cardiovascular deconditioning; and fluid redistribution. These lead to orthostatic intolerance; immune suppression; increased infection risk; exposure to ionizing radiation, which poses elevated risks of malignancy and neurodegenerative diseases; gastrointestinal dysfunction; gut dysbiosis; and cognitive, psychological, and sleep disturbances. These multifaceted stressors necessitate holistic and multitarget countermeasures, particularly as extended missions have become a reality. Japanese Traditional (Kampo) medicines, composed of multiple herbal ingredients, offer a promising supportive strategy because of their efficacy in simultaneously managing diverse physical and psychological symptoms. Kampo formulas exert diverse benefits. For example, Ninjin’yoeito, Juzentaihoto, Hochuekkito, and Goshajinkigan promote musculoskeletal strength and prevent frailty. Goreisan regulates fluid and cardiovascular homeostasis; Hochuekkito supports immune modulation and infection prevention; Rikkunshito address gastrointestinal symptoms via appetite and digestive regulation; and Yokukansan and Kamikihito mitigate neurological and psychological disturbances. Another benefit is the regulation of the gut microbiota, mitochondrial function, and gene expression, which are relevant to space-induced metabolic and inflammatory alterations. The holistic, preventive, and safe profile of Kampo medicines make them well suited for integrated health management in space, facilitating the maintenance of performance and quality of life during long-duration missions. Ongoing research and advances in personalized medicine will help clarify and optimize the application of Kampo in space medicine.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1839225</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1839225</link>
        <title><![CDATA[Machine learning analysis of cross-industry innovation efficiency: evidence from Chinese listed companies (2006–2023)]]></title>
        <pubdate>2026-07-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Tan Yang</author><author>Haiqing Hu</author><author>Pufeng Wu</author><author>Huanqing Liu</author>
        <description><![CDATA[BackgroundHow listed firms convert R&D spending into patent outputs is central to innovation management and applied econometrics. We treat InnoEff1 as a reduced-form innovation-conversion indicator—not a DEA/SFA frontier-efficiency score.MethodsUsing 40,706 Chinese listed firm-years (2006–2023) across 20 industry segments, we prioritize a restricted-variable Gradient Boosting specification that excludes contemporaneous patent stocks overlapping the outcome numerator. Validation combines five-fold GroupKFold blocking by firm, a strict 2019–2023 time hold-out, DEA/SFA-style benchmarks, and industry-balanced subsample checks.ResultsUnder GroupKFold, the restricted model attains R2 ≈ 0.414 (time hold-out ≈0.144), versus ≈0.989 (hold-out ≈0.978) when patent overlaps are retained—quantifying mechanical fit inflation. Tabulated cross-industry mean InnoEff1 spans 0.088 (Table 1); Kruskal–Wallis rejects equal distributions (H ≈ 2133, P < 10−10), and 28 of 45 Table-1 pairwise contrasts remain significant after Holm–Bonferroni adjustment. Lagged R&D intensity and financial covariates dominate SHAP attributions in the restricted model.ConclusionThe contribution lies in validated machine-learning practice—leakage control, interpretability, and transparent benchmarking—not in near-unity R2 diagnostics. Predictive patterns are associative; they do not justify causal claims that broad-based policies dominate sector-specific innovation support.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1843325</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1843325</link>
        <title><![CDATA[Evaluation of dosimetric parameters in three volumetric modulated arc therapy regimens for hippocampal and scalp sparing in whole-brain radiation therapy]]></title>
        <pubdate>2026-07-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Huaqu Zeng</author><author>Yangxing Lin</author><author>Zongyou Chen</author><author>Shukui Tang</author><author>Qifu Lin</author><author>Zunbei Wen</author>
        <description><![CDATA[ObjectiveTo evaluate the dosimetric performance of three Volumetric Modulated Arc Therapy (VMAT) techniques with different beam arrangements—Coplanar Partial-Field VMAT (PF-VMAT), Non-Coplanar VMAT (NC-VMAT), and Coplanar Whole-Field VMAT (WF-VMAT)—for hippocampal and scalp sparing in whole-brain radiotherapy (WBRT), and to provide a basis for selecting the optimal clinical treatment plan.MethodsData from 22 patients receiving WBRT were retrospectively analyzed. Based on the RTOG 0933 protocol, PF-VMAT, NC-VMAT, and WF-VMAT plans were designed for each patient using the Eclipse treatment planning system. The three plan groups were compared regarding target coverage, dose homogeneity, conformity, and radiation dose to organs at risk (OARs) including the hippocampus, lenses, optic nerves, and scalp. Statistical analysis was performed.ResultsAll plans met the RTOG 0933 dose constraints. For the PTV, PF-VMAT demonstrated significantly lower D2% and D50% compared to the other two techniques (p < 0.05). Regarding hippocampal sparing, both PF-VMAT and NC-VMAT achieved significantly lower Dmin and Dmean to the hippocampus compared to WF-VMAT (p < 0.05). For other OARs, NC-VMAT significantly reduced the Dmax to the lenses, optic chiasm, and scalp (p < 0.05). PF-VMAT required significantly higher monitor units (MU) than the other two plans (p < 0.05).ConclusionNC-VMAT offers significant dosimetric advantages for scalp and visual structure sparing. It is particularly suitable for patients requiring high protection for these organs, albeit with increased treatment complexity and time due to its non-coplanar nature. PF-VMAT provides hippocampal sparing comparable to NC-VMAT and superior control of high-dose regions (lower D2%) but at the cost of higher MU. WF-VMAT is less effective in protecting OARs compared to NC-VMAT and PF-VMAT. Considering the balance between dosimetric gain and practical feasibility, PF-VMAT represents a pragmatic choice for routine clinical use, as it provides adequate target coverage and OAR sparing while eliminating the need for couch rotations during treatment.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1768372</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1768372</link>
        <title><![CDATA[Artificial intelligence: unpredictable or unprestatable?]]></title>
        <pubdate>2026-07-08T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Andrea Roli</author><author>Sauro Succi</author><author>Stuart A. Kauffman</author>
        <description><![CDATA[Current AI technologies have demonstrated impressive results, mainly driven by large language models (LLMs). The most diffused applications of LLMs are in the so-called generative AI, which consists in techniques that produce texts, music, pictures or videos–often in a multimodal setting. Challenging the intuition that machines cannot be truly creative, the artefacts produced by LLMs are sometimes considered as surprising, novel and creative. This view is also supported by observing that there are both theoretical and practical limitations on the predictability of AI systems’ outcomes. Actual creativity can also be transformative and inventive, hence not just unpredictable but unprestatable: true novelty arises within a process whose evolution of the very possibility space cannot be predicted. Prominent examples of unprestatability are the evolution of the biosphere and can be found in artistic human productions. In this contribution, we elaborate on the notions of predictability and prestatability in the context of current AI systems. We maintain that these systems are, to some extent, unpredictable but not unprestatable. A consequence of our contention is the definition of the limits of what AI systems can and cannot do, and therefore the contexts for which these technologies are best suited.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1884191</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1884191</link>
        <title><![CDATA[A density-aware path-integral and forward-scattering imaging model for single-image dehazing in non-homogeneous fog]]></title>
        <pubdate>2026-07-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yicheng Ou</author><author>Hanchu Guo</author><author>Qihang Shao</author><author>Xinyu Wang</author>
        <description><![CDATA[Single-image dehazing through non-homogeneous fog is an ill-posed inverse problem at the interface of imaging through scattering media and real-time perception. It raises two coupled difficulties, namely, spatially inconsistent degradation in which thin and dense fog coexist at different depths and the need for a controllable inverse solution under a tight latency budget. Most physics-guided networks estimate transmission implicitly and apply roughly uniform restoration across the scene, without jointly modeling path-integral extinction and forward-scattering diffusion under a single density field. Attention- and transformer-based methods raise the restoration quality but incur an order-of-magnitude increase in the inference cost. We propose UPFS-Dehaze, which couples a density-aware path-integral imaging formulation with a latency-aware unrolled optimization module governed by a spatially weighted Barzilai–Borwein step-size field. Within a fixed-depth three-stage update chain, path-integral extinction and forward-scattering diffusion are jointly modeled through a single estimated haze density field; forward scattering enters as a learned density-modulated scattering field that serves as a tractable surrogate for the underlying kernel integral. The result is region-adaptive restoration at an explicitly bounded inference cost. On the synthetic RESIDE SOTS-outdoor benchmark, the model attains 29.66 dB PSNR and 0.972 SSIM at 0.018 s per image, placing it on the upper-left Pareto frontier of the quality–efficiency trade-off. On two real-fog benchmarks—O-HAZE and the more challenging NH-HAZE (NTIRE 2020)—it attains the highest absolute PSNR among the evaluated zero-shot baselines, with a synthetic-to-real PSNR drop of 9.55 dB on O-HAZE and 10.54 dB on NH-HAZE, against 17.14 dB and 18.10 dB for the highest-PSNR synthetic baseline. Together, these results indicate that unifying extinction and forward scattering under a single density field and solving the inverse problem with a latency-bounded unrolled optimizer supports image restoration through non-homogeneous scattering media without sacrificing real-time inference.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1845540</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1845540</link>
        <title><![CDATA[Hybrid data-driven optimization of electrical performance in 10 MW-class wind turbines: a quantitative multi-physics coupling approach]]></title>
        <pubdate>2026-07-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Haonan Cui</author><author>Lipeng Yan</author><author>Zhizheng Chen</author>
        <description><![CDATA[The aim of this study is to propose a real-time multiphysics control framework for large-scale offshore wind turbines for a hybrid Model Predictive Control, Reinforcement Learning and Genetic Algorithms system. The goal is to achieve optimal aerodynamic efficiency, minimize structural fatigue loads and improve the quality of electrical power generated under stochastic wind conditions. The proposed framework combines four coupled subsystems: aerodynamic model based on blade element momentum theory, finite-element load dynamics model, electrical generator model (MATLAB/Simulink), and hybrid intelligent control model (MATLAB/Simulink and Python). The control strategy is based on three concepts: constrained predictive optimization in Model Predictive Control, adaptive policy learning in Reinforcement Learning and global parameter tuning in Genetic Algorithms. The validation of the benchmark is carried out using a Proportional-integral-derivative baseline and turbine dynamic behavior consistent with OpenFAST to ensure the physical realism of the benchmark. The simulation results show that the proposed method provides a power improvement of about 12.3% compared to the conventional method and reduces the structural loads and electrical fluctuation by nearly 30% in various wind regimes. The proposed framework is compared with the existing Model Predictive Control and Reinforcement Learning only approaches, which are not capable of real-time optimization for aerodynamic, structural, and electrical coupling. The findings validate the effectiveness of the proposed approach for next-generation offshore wind turbine control systems for highly variable wind conditions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1880894</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1880894</link>
        <title><![CDATA[Ab initio nuclear theory for heavy nuclei and its application to dark matter-nucleus scattering]]></title>
        <pubdate>2026-07-03T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Bai-Shan Hu</author>
        <description><![CDATA[The era of precision ab initio nuclear theory has arrived, enabling uncertainty-quantified predictions for nuclear structure and for interactions with external probes directly from the underlying nuclear force and electroweak currents. This review highlights recent breakthroughs that extend ab initio calculations to the heavy nucleus 208Pb, to medium-mass systems with complex deformation, and to weakly-bound nuclei near the driplines. We also summarize ab initio calculations of nuclear responses for dark matter direct detection. Together, these advances demonstrate how ab initio methods can substantially reduce nuclear-physics uncertainties in searches for physics beyond the Standard Model, providing a more robust interpretation of current and forthcoming precision experiments.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1867671</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1867671</link>
        <title><![CDATA[Residual-triggered adaptive strong tracking Kalman filter for acousto-optic modulator-based laser power stabilization]]></title>
        <pubdate>2026-07-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Lipeng Han</author><author>Guohua Zhong</author><author>Long Chen</author><author>Xindong Liang</author><author>Xiongfei Yin</author>
        <description><![CDATA[High laser power stability is critical for precision interferometry and laser frequency stabilization. Acousto-optic modulators (AOMs) are widely used for laser power control due to their fast response and high linearity; however, their diffraction efficiency is sensitive to environmental temperature variations and self-heating, limiting long-term stability. This work presents a closed-loop laser power stabilization scheme based on a residual-triggered adaptive strong tracking Kalman filter (RT-ASTKF), operating without temperature-control hardware. The algorithm employs a fixed process-noise covariance together with adaptive measurement-noise estimation, enabling real-time compensation of AOM thermal drift and circuit noise. The system is implemented on an STM32H743 platform for synchronous acquisition and RF gain control. In a 13.9-h experiment, the closed-loop system achieves an RMS stability of 0.0615% and a peak-to-peak fluctuation of 0.391% (following 3σ outlier removal), while the Allan deviation remains at the 10−8–10–9 level for integration times of 102–104 s. The open-loop periodic drift (4.363% peak-to-peak, 0.3780% RMS) is suppressed to 0.391% peak-to-peak and 0.0615% RMS, corresponding to 11.16-fold peak-to-peak and 6.15-fold RMS reduction (approximately 20.9 dB and 15.8 dB, respectively). These results demonstrate effective suppression of compound disturbances without additional thermal-control hardware.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1897490</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1897490</link>
        <title><![CDATA[Editorial: Advancements in instrumentation and detector modeling for TOF-based medical imaging]]></title>
        <pubdate>2026-07-02T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Nicolaus Kratochwil</author><author>Andrea Gonzalez-Montoro</author><author>Vanessa Nadig</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1859498</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1859498</link>
        <title><![CDATA[Analysis and Laplace transformation for 2D temporal arbitrary order Rayleigh Stokes problem for a heated generalized second grade fluid equation]]></title>
        <pubdate>2026-07-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Madiha Batool</author><author>Amal Alshabanat</author><author>Saifudin Hafiz Yahaya</author><author>Umair Ali</author><author>Hijaz Ahmad</author>
        <description><![CDATA[In this work, an efficient computational scheme for handling the two-dimensional time-fractional Rayleigh-Stokes problem is presented. The Riemann–Liouville fractional derivative operator is used to simulate temporal memory effects in fluid dynamics. Two major innovations are included in the proposed methodology: first, the Laplace transform is used to convert the Riemann–Liouville fractional derivative into an equivalent integer-order formulation; then, a finite difference scheme for extremely accurate temporal and spatial discretization is developed. To verify the reliability of the chosen numerical scheme, thorough stability and consistency analyses are carried out. The method’s accuracy and computing efficiency are demonstrated by numerical experiments, and the comparison outcomes are in good agreement with current methods. A trustworthy and effective computational tool for simulating fractional fluid flow phenomena governed by nonlocal temporal dynamics is offered by the proposed framework.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1884310</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1884310</link>
        <title><![CDATA[Impact of IrOx membrane on HfOx-based capacitor platform integrated with convolutional spiking neural networks for future clinical biomarker diagnostics]]></title>
        <pubdate>2026-07-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Abhijit Aich</author><author>Long Nguyen Minh Le</author><author>Sourav Roy</author><author>Yi-Pin Chen</author><author>Shih-Yin Huang</author><author>Siddheswar Maikap</author>
        <description><![CDATA[This study investigates the detection of hydrogen peroxide (H2O2) and enzymatic urea using a novel IrOx membrane-based IrOx/HfOx/SiO2/p-Si electrolyte-insulator-semiconductor (EIS) platform. The chemical properties, including the stable oxidation states of the sensing membranes, were confirmed through X-ray photoelectron spectroscopy (XPS) analysis, which identified the transitions from Ir0 to Ir4+ and the presence of mixed valence states (Hf2+, Hf3+, and Hf4+). The IrOx-based EIS sensor exhibited a super-Nernstian pH sensitivity of 72 mV/pH and excellent linearity of 99.9%. Furthermore, the sensor demonstrated high sensitivity to enzymatic urea, with a detection limit of 100 pM. For H2O2 sensing, the device achieved a low detection limit of 0.5 pM over a linear range of 0.5–500 pM, attributed to redox reactions at the electrolyte-insulator interface. To further enhance the diagnostic capability, a Convolutional Spiking Neural Network (CSNN) simulation was employed to classify the biomarker detection results into 3 different thresholds, achieving a high classification accuracy of 95.05%. These results indicate that the IrOx/HfOx-based EIS platform, combined with spiking neural network analysis, is promising for future point-of-care diagnostic applications and early-stage disease detection with minimal sample volumes. The proposed nanostructured IrOx/HfOx sensing platform also shows great potential for next-generation medical diagnostics driven by AI, consistent with the growing global emphasis on cost-effective and sustainable point-of-care healthcare. Furthermore, this research supports the goals of United Nations Sustainable Development Goal 3 (Good Health and Wellbeing) and SDG 9 (Industry, Innovation, and Infrastructure) by creating intelligent, nanotechnology-based biosensing systems for personalized healthcare and early disease detection.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1809470</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1809470</link>
        <title><![CDATA[Regional analysis of study abroad search indices in China based on visibility graph theory: temporal patterns and regional coordination]]></title>
        <pubdate>2026-07-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Li Wang</author><author>Jun-Chao Ma</author>
        <description><![CDATA[IntroductionUnderstanding the temporal dynamics and regional variation of study-abroad search attention is important for interpreting educational mobility intentions in a highly digitized information environment.MethodsThis study applies visibility graph theory to Douyin/Juliang Suanshu study-abroad search indices across 31 mainland Chinese provincial-level units from 4 June 2022 to 31 May 2025. Provincial series are aggregated into seven major regions, with summation used as the primary aggregation method and PCA used for aggregation sensitivity analysis. Regional natural visibility graphs are benchmarked against 100 size- and density-matched random graphs, alternative degree distributions are fitted, and regional network complexity is evaluated using the entropy weight method (EWM) with bootstrap uncertainty.ResultsThe results show that all regional visibility graphs have substantially higher clustering than random benchmarks and small-world coefficients above 42, while the degree distributions are better interpreted as heavy-tailed than as uniquely confirmed power laws. Regional time series are strongly synchronized, with a mean off-diagonal zero-lag correlation of 0.987 and no systematic lead-lag pattern within a 30-day window. EWM ranks Central China highest in the daily analysis, followed by East China and South China, but bootstrap intervals overlap and the ordering is sensitive to weekly aggregation. Weekly visibility-graph community detection identifies 5–7 temporal communities per region and recurring transition dates around February 2023, September 2023, March 2024, and late 2024.DiscussionThese findings clarify the temporal organization of study-abroad search attention and provide a network-based framework for analyzing regional educational search behavior.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1837668</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1837668</link>
        <title><![CDATA[Public investment search behavior as an external attention signal: visibility-graph evidence from Douyin data in Shandong Province]]></title>
        <pubdate>2026-06-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mingwei Cui</author><author>Shiwen Sun</author><author>Hao Wang</author>
        <description><![CDATA[IntroductionPublic search behavior provides a high-frequency external attention signal for understanding changes in market expectations in the digital economy. During periods of macroeconomic adjustment and investment uncertainty, search attention may capture shifts in public concern, information demand, and expectation formation.MethodsUsing Douyin search data on the theme of “investment” in Shandong Province from 4 June 2022 to 1 August 2025, this study constructs a Public Investment Search Network based on the Visibility Graph algorithm. The analysis examines temporal fluctuation, phase-based evolution, network topology, community differentiation, topological indicators, degree distribution, and robustness under alternative network constructions.ResultsThe results show clear phase-based aggregation and divergence in public investment attention. The network exhibits a heavy-tailed degree distribution and small-world-like characteristics. Attention evolves through a cyclical process of concentration, dispersion, and rebalancing under the combined influence of policy stimuli, market fluctuations, and information diffusion. Changes in clustering coefficient, modularity, volatility, and Shannon entropy further reveal the self-organizing features of public investment search behavior.DiscussionThe findings suggest that the public investment search network provides a structural representation of collective attention and offers supplementary information for monitoring market signals and changes in public expectations. The study describes the structure of public investment-related attention rather than directly testing firm-level investment responses. Future research may combine search-network indicators with firm-level investment, innovation, or financial data to further examine how external attention signals are incorporated into corporate decisions.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1863704</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1863704</link>
        <title><![CDATA[Novel design of a Lobatto IIIA data-driven intelligent Levenberg–Marquardt backpropagation scheme for predictive modelling of heat transfer and thermal efficiency in micropolar hybrid nanofluids]]></title>
        <pubdate>2026-06-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Syed Modassir Hussain</author><author>Umair Khan</author><author>Hami Gündoğdu</author><author>Aurang Zaib</author><author>Najiyah Safwa Khashi’ie</author><author>Jomana A. Bashatah</author>
        <description><![CDATA[It is well-known that blood is treated as a non-Newtonian fluid because its viscosity can vary with shear stress. The present study explores the effect of melting and stagnation-point micropolar fluid flow by involving blood-based copper (Cu)/copper-oxide (CuO) hybrid nanofluids and the features of heat transport across a moving sheet with inertial and microstructure characteristics. Initially, the problem is modelled in the form of partial differential equations and then changed into ordinary differential equations by using similarity variables. These equations are numerically solved using the fourth-order boundary value solver bvp4c and a neural network depending on the algorithm of the Levenberg-Marquardt back-propagation. The outcomes revealed that the suggested artificial neural network method could hold nonlinear data with minimum error and showed consistent performance across all phases, including training, validation, and testing. In addition, the friction factor and the micro-rotation coefficient increase significantly by up to 5.602% and 7.701%, respectively, with increasing nanoparticle volume fractions. In contrast, the heat transfer rate shows only very small variations with increasing nanoparticle volume fractions and changes in other influential parameters because of the imposed melting boundary conditions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1864040</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1864040</link>
        <title><![CDATA[Analysis of IIoT data using visibility graphs and complex network methods]]></title>
        <pubdate>2026-06-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jiwei Xu</author><author>Kaigong Wang</author><author>Jiaqi Wang</author><author>Jiayu Wu</author><author>Xinyan Lv</author><author>Qun Song</author>
        <description><![CDATA[With the increasing volume of time series data generated in the Industrial Internet of Things (IIoT), characterizing the intrinsic structural relationships within such data has become a key challenge in complex system analysis. The visibility graph (VG) method maps time series into complex networks, enabling the representation of dynamic features in a topological form and providing an effective tool for nonlinear time series analysis. Compared with traditional approaches that primarily focus on statistical features, VG can reveal latent structural information and correlation patterns within the sequence. Based on this, the VG method is applied in this study to the analysis of power consumption time series from electric vehicle charging stations. The time series are transformed into complex networks, and their structural characteristics are systematically investigated from a complex network perspective. By constructing visibility graph networks of the power consumption sequences, statistical analyses of node degree, degree distribution, and overall topological structure are performed to characterize differences in system structure under different operating states and scenarios. The results show that the constructed VG networks exhibit pronounced structural heterogeneity. The node degree distributions display clear heavy-tailed behavior and approximately follow a power-law scaling within a certain range. Meanwhile, a small number of highly connected nodes play a dominant role in the overall network structure, while most nodes remain weakly connected. Further analysis reveals significant structural differences across operating conditions, reflecting the complex dynamic evolution of charging behavior under different scenarios. On this basis, classification models are built by combining statistical features with VG-based structural features for validation. The results demonstrate that incorporating VG features can improve classification performance to a certain extent, indicating that the VG method provides additional structural information for time series analysis. Overall, the proposed approach offers a useful framework for structural modeling and anomaly detection of IIoT time series data.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1865724</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1865724</link>
        <title><![CDATA[Editorial: Security, governance, and challenges of the new generation of cyber-physical-social systems, Volume II]]></title>
        <pubdate>2026-06-26T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Yuanyuan Huang</author><author>Jianping Gou</author><author>Xin Lu</author><author>Amin Ul Haq</author><author>Qifei Wang</author><author>Jiazhong Lu</author>
        <description></description>
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