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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>2026-10-05T11:20:59.891+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1916847</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1916847</link>
        <title><![CDATA[Inverse design of multi-mode OAM-generating pixelated THz metasurfaces]]></title>
        <pubdate>2026-10-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ruilu Huang</author><author>Jiusheng Li</author><author>Guo-Hua Qiu</author>
        <description><![CDATA[IntroductionTraditional metasurface design relies on physical priors and parameter scanning, which is time-consuming and prone to getting stuck in local optima.MethodsIn this article, we proposed a pixelated metasurface inverse design method, which discretizes the metasurface elements into a 16 × 16 binary pixel array, with each pixel independently adjustable, expanding the design space. We constructed a deep neural network framework consisting of a forward prediction residual convolutional network and a tandem generative inverse network: The forward network takes the element structure image as input and quickly predicts its complex reflection coefficient spectrum.Results and DiscussionThe average mean square error (MSE) of the test set is 0.0017. The inverse network is implemented by concatenating pre-trained forward networks without the need for real geometric labels, with an average MSE of 0.0131 on the test set. Using the trained model, we designed a metasurface that generates orbital angular momentum (OAM) with topological charges of ±1 and ±2 in the 1.5-1.6 THz frequency range. The full wave simulation results confirm that the designed metasurface can achieve spatial separation and independent control of multi-topology OAM beams, and the far-field amplitude and phase distribution are consistent with theoretical expectations. This method significantly improves the design efficiency and control accuracy of multifunctional metasurfaces, providing a new approach for on-demand customization of terahertz wave devices.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1941515</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1941515</link>
        <title><![CDATA[Evolutionary game analysis of scientific research data sharing under the open science context]]></title>
        <pubdate>2026-10-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jie Zhang</author><author>Jian Yang</author>
        <description><![CDATA[Scientific data sharing is central to open science, yet its development is constrained by fragmented data resources, heterogeneous stakeholder incentives, privacy risks, and limited regulatory capacity. This study develops a bounded-rationality, four-population evolutionary game framework involving data providers, data users, government agencies, and sharing platforms. The model constructs the strategic payoff structure among these actors, derives the corresponding replicator dynamics, and examines the local stability of boundary equilibria through Jacobian eigenvalue analysis. Numerical simulations based on the same dynamic equations are used to illustrate convergence tendencies and parameter sensitivity, rather than to provide independent empirical validation. The results show that cooperative data-sharing behavior can emerge when reputational gains, user benefits, platform incentives, and regulatory instruments are jointly sufficient to offset sharing costs, verification burdens, and privacy-related losses. The analysis further suggests that lower-intensity government involvement can coexist with active participation by providers, users, and platforms, provided that incentive and penalty mechanisms remain in place. Privacy risk is identified as a key boundary condition that may disrupt cooperative sharing when it becomes excessive, while over-intensified subsidies may yield diminishing behavioral returns once strategic probabilities approach stable boundary states. Because the platform payoff and fiscal constraints are deliberately simplified, the findings should be interpreted as model-based policy insights for phased regulation, privacy-risk governance, and incentive design rather than as direct empirical prescriptions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1927772</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1927772</link>
        <title><![CDATA[Solar and geomagnetic activity modulation of wave–particle interactions in Earth’s radiation belts]]></title>
        <pubdate>2026-10-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Qi Zhu</author><author>Zhiqiu He</author>
        <description><![CDATA[Solar forcing and geomagnetic disturbances reshape radiation-belt electron populations through a chain of coupled processes that cannot be represented adequately by a single activity index. This study introduces an activity-conditioned resonance kernel (ACRK) that links solar-wind driving, magnetic compression, cold-plasma density, and wave spectra to pitch-angle and energy diffusion. The formulation distinguishes rapid event-scale modulation from slower background recovery, yields a bounded diffusion closure, and provides a sufficient condition for the sign of local energization. Controlled numerical experiments demonstrate threshold-like changes in resonance access and lower trajectory mismatch than static-spectrum and event-driven quasi-linear models. The study is deliberately based on reproducible synthetic experiments rather than a new satellite-data reanalysis. The proposed framework clarifies the conditions under which geomagnetic activity favors local acceleration, scattering, or mixed responses in Earth’s outer radiation-belt.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1953080</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1953080</link>
        <title><![CDATA[An AI agent-based smart campus management platform]]></title>
        <pubdate>2026-10-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xin Yue</author><author>Zhuo Ma</author><author>Jianqing Liu</author><author>Guangming Bo</author><author>Zhiyang Qiu</author>
        <description><![CDATA[The continuing digital transformation of higher education has produced large volumes of heterogeneous campus data, including network access records, wireless access-point mappings, academic information, security alerts, and institutional documents. These resources are often isolated across operational systems and therefore remain difficult to trace, integrate, interpret and use in a timely manner. This paper presents the design of an AI agent-based smart campus management platform intended to convert such fragmented data into a governed and explainable basis for campus operations and student services. The proposed work combines multisource data acquisition, layered data governance, Wi-Fi-based student trajectory reconstruction, spatiotemporal behavior analysis, anomalous traffic detection, interactive visualization, automated reporting, and conversational analysis. Its technical route separates transactional and analytical workloads through MySQL and ClickHouse, uses Kafka for stream ingestion and decoupling, employs Redis and scheduled jobs for responsive services, and introduces an AI Agent with retrieval-augmented generation (RAG) for tool-mediated data queries and knowledge-grounded responses. The platform is organized as a modular, independently deployable system for a single institution and incorporates role-based access control, desensitization, audit trails, and controlled knowledge publication. The expected value is a reusable architecture that supports campus situational awareness, interpretable student activity analysis, earlier risk identification, evidence-informed resource planning, and lower-barrier access to institutional analytics.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1944084</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1944084</link>
        <title><![CDATA[Atmospheric-pressure cold plasma assisted enhancement of germination and nutritional trait of peanut seeds]]></title>
        <pubdate>2026-09-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sushma Jangra</author><author>Ritesh Mishra</author><author>Abhijit Mishra</author><author>Shikha Pandey</author><author>Ram Prakash</author>
        <description><![CDATA[IntroductionImproving seed germination and early seedling growth using environmentally friendly, chemical-free technologies is essential for enhancing crop productivity and agricultural sustainability. This study investigated the effects of low-power dielectric barrier discharge (DBD)-based ACP treatment on the physicochemical characteristics, germination, antioxidant activity, and early seedling growth of peanut seeds.MethodsPeanut seeds were treated with DBD-based ACP for 10, 20, 30, 40, and 50 s. Optical emission spectroscopy (OES) was used to identify reactive oxygen and nitrogen species generated during plasma treatment. The effects of different plasma exposure times on seed germination, seedling growth, vigor, wettability, antioxidant activity, elemental composition, functional groups, and surface morphology were evaluated. Antioxidant activity was determined using DPPH and ABTS assays, while Fourier-transform infrared spectroscopy (FTIR), inductively coupled plasma mass spectrometry (ICP-MS), and field-emission scanning electron microscopy (FESEM) were used to characterize the treated seeds and sprouts.ResultsAmong the tested exposure times, 40 s was identified as the optimal plasma treatment condition. Compared with untreated seeds, the optimized treatment significantly increased the germination rate, seedling growth, germination potential, germination index, vigor index, and seed wettability by 9.67%, 45.13%, 11.09%, 13.85%, 61.77%, and 28.98%, respectively. DPPH antioxidant activity increased from 2.495 to 2.850 mg Trolox/g DW, while ABTS activity increased from 6.195 to 6.430 mg Trolox/g DW. ICP-MS analysis showed increases in Na, B, Ca, Mg, Al, and Fe contents of 77.80%, 54.37%, 72.92%, 35.41%, 75.77%, and 65.81%, respectively, in treated peanut sprouts.DiscussionThe results demonstrate that brief exposure to low-power DBD-based ACP, particularly for 40 s, can effectively enhance peanut seed germination, vigor, seed wettability, antioxidant activity, elemental composition, and early seedling growth. These improvements are associated with plasma-induced changes in reactive species, seed surface morphology, and physicochemical characteristics. The findings highlight the potential of ACP as an environmentally friendly, chemical-free, and scalable technology for seed treatment and agricultural applications.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1924637</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1924637</link>
        <title><![CDATA[Use of an ANN–Levenberg–Marquardt optimization for radiative hybrid nanofluids and heat transfer efficiency over non-isothermal wedge and cone surfaces with chemical reaction effects]]></title>
        <pubdate>2026-09-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Bhavanam Naga Lakshmi</author><author>G. Swamy Reddy</author><author>Chundru Maheswari</author><author>V. Sujatha</author><author>Asra Anjum</author><author>Umair Khan</author><author>J. U. Viharika</author><author>Samia Elattar</author>
        <description><![CDATA[The advancement of thermal engineering technologies is driven by the increasing requirement for efficient energy utilization and enhanced heat transfer performance in various industrial sectors. The present study emphasizes the systematic examination of hybrid nanofluid magnetohydrodynamic flow across the geometries of two different surfaces—a cone and a wedge—within a porous medium while considering thermal radiation and chemical reactions. This study is governed by PDEs, which are consequently transformed into ODEs through suitable similarity transformations. Mathematical solutions are derived utilizing the bvp5c method within a MATLAB environment. The upsurge in thermal radiation and Brownian motion parameter enhances the thermal profile of the hybrid nanofluid. The mass transfer rate of the hybrid nanofluid decreases with respect to the Schmidt number, and the chemical reaction enhances for thermophoresis. An artificial neural network based on the Levenberg–Marquardt backpropagation scheme (ANN-LMBPS) was employed, and its accuracy was validated against the bvp5c results for the effects of magnetism, porosity, thermophoresis, Brownian motion, and chemical reaction.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1908805</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1908805</link>
        <title><![CDATA[Does regional competition hinder renewable energy innovation? evidence from Chinese cities]]></title>
        <pubdate>2026-09-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Shi Chen</author><author>Xue Lei</author><author>Jiaxin Gao</author><author>Jian Xu</author>
        <description><![CDATA[Traditional models of economic performance that emphasize factor accumulation tend to overlook environmental considerations, while the role of innovation is often marginalized in the context of environmental governance. This study connects the two by asking how competition among local governments shapes urban renewable energy technological innovation (RETI). Building on a systematic analysis of the mechanisms linking regional competition and urban RETI, this study empirically examines the impact of regional competition on RETI using a comprehensive panel of Chinese prefecture-level cities, addressing endogeneity with a Bartik-type instrument. The results indicate that regional competition exerts a significantly negative effect on urban RETI. Channel analysis suggests that this effect operates sequentially: competition expands low-technology manufacturing, which widens the dispersion of marginal returns to labour, and is summarised in a decline in green economic efficiency, which reflects the shift in industrial composition that the two preceding channels describe. Moreover, the inhibiting effect of regional competition on RETI is more pronounced in cities characterized by higher levels of digital development and environmental pollution, as well as weaker environmental regulation. Because these disadvantages are mutually reinforcing, the resulting dispersion in clean-technology capability is unlikely to be self-correcting, which makes the timing of intervention consequential. We interpret our estimates as the average reduced-form effect of competitive pressure on innovation outcomes rather than as evidence on the dynamics of inter-city strategic interaction. These findings contribute to a deeper understanding of inter-city competitiveness and provide policy-relevant insights for the differentiated formulation and coordination of green development objectives across cities.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1889727</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1889727</link>
        <title><![CDATA[Risk assessment of thyroid cancer recurrence based on quantitative multiphoton imaging]]></title>
        <pubdate>2026-09-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Qiuyan He</author><author>Zilu Lin</author><author>Han Wu</author><author>Jiajia He</author><author>Gangqin Xi</author><author>Zhizhong Chen</author><author>Zhijie You</author><author>Juan Lin</author><author>Ang Chen</author><author>Guangxing Wang</author><author>Shuangmu Zhuo</author>
        <description><![CDATA[Thyroid cancer is a common endocrine malignancy with rising incidence, and although its overall prognosis is relatively favorable, postoperative recurrence remains a clinical challenge due to limited insight into tumor microstructure and stromal remodeling from current risk assessment methods. To address this issue, this study investigated the association between collagen fiber morphological and textural characteristics and tumor recurrence using multiphoton microscopy (MPM), a high-resolution, label-free imaging technique capable of visualizing tissue microstructure and stromal architecture in detail, providing detailed quantitative information on both cellular and extracellular components. To identify key features linked to recurrence, a multi-stage pipeline-univariate testing, correlation-based feature reduction, and least absolute shrinkage and selection operator regression were employed. The distributions of the selected features were visualized using box plots and kernel density curves. Finally, based on the selected features, a logistic regression model was developed and its ability to discriminate between high- and low-risk patients was evaluated using stratified five-fold cross-validation, with performance quantified by the receiver operating characteristic curve and the area under the curve (AUC). Qualitatively MPM imaging revealed pronounced collagen linearization and aligned fiber remodeling in high-risk tissues, contrasting with the wavy, interwoven architecture of low-risk tissues. Through the feature selection process, ten key features were identified, all showing significant distributional differences between risk groups (P < 0.0001). The resulting logistic regression model achieved strong discrimination, with an AUC of 0.959 in cross-validation. These findings demonstrate that the developed model effectively stratifies recurrence risk in thyroid cancer and may serve as an objective, quantitative tool to support individualized postoperative monitoring and prognostic assessment.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1908603</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1908603</link>
        <title><![CDATA[Nuclear quantum effects and proton transport in equimolar water–phosphoric acid mixtures]]></title>
        <pubdate>2026-09-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>N. V. Plechkova</author><author>F. Fernandez-Alonso</author><author>F. D’Anna</author><author>F. Billeci</author><author>F. M. Ferrero Vallana</author><author>K. R. Seddon</author><author>M. Krzystyniak</author>
        <description><![CDATA[IntroductionWe investigated isotope effects associated with nuclear quantum dynamics in equimolar water–phosphoric acid mixtures and their relationship to the large low-temperature isotope effect previously reported for proton conductivity.MethodsNeutron Compton scattering measurements were performed using the VESUVIO beamline at the ISIS Neutron and Muon Source, Rutherford Appleton Laboratory, UK. Nuclear kinetic energies and momentum distributions were determined for hydrogen and deuterium.ResultsPronounced isotope dependence was observed in the nuclear kinetic energies of H and D, together with non-Gaussian components in their nuclear momentum distributions. These signatures are consistent with an anharmonic, barrierless local effective Born–Oppenheimer potential rather than a double-well potential.DiscussionThe microscopic observations provide a local nuclear-dynamical counterpart to the reported isotope effect in proton conductivity. The transport behaviour is consistent with zero-point-energy-driven lowering of the effective proton-hopping barrier rather than tunnelling-dominated motion. Although smaller, site-specific tunnelling contributions cannot be excluded, the site- and isotope-averaged neutron Compton scattering observables do not require tunnelling to be the dominant transport mechanism. These findings demonstrate neutron Compton scattering as an isotope-selective diagnostic of nuclear quantum effects in an ionic-liquid-like proton-conducting medium and establish a framework for applying nuclear momentum distributions to disordered hydrogen-bonded electrolytes and glassy proton conductors.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1903608</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1903608</link>
        <title><![CDATA[Deep learning-based trustworthy dispatch model for virtual power plants in megacity power grids]]></title>
        <pubdate>2026-09-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yuandong Jiang</author><author>Mingyu Ou</author><author>Jiangnan Li</author>
        <description><![CDATA[A deep learning-based trustworthy dispatch model for virtual power plants (VPPs) operating in megacity power grids is proposed to address the interrelated challenges of data scarcity, trust deficits, and operational uncertainty. The proposed framework integrates a hybrid data augmentation module combining a physically constrained generative adversarial network with temporal consistency regularization, a blockchain-anchored multi-dimensional trust evaluation mechanism, and a Wasserstein distance-based distributionally robust optimization (DRO) formulation. A hierarchical dispatch architecture is established, coupling day-ahead DRO with real-time model predictive control, while multi-agent deep reinforcement learning coordinates distributed energy resources under partial observability. Experimental validation is conducted using 18-month operational data from a VPP demonstration project in eastern China, comprising 45 photovoltaic systems, 28 wind turbines, 15 battery storage units, and 120 flexible load aggregators. The proposed model achieves a root mean square tracking error of 0.43 MW, representing a 53% reduction compared with a deep reinforcement learning baseline, and attains a renewable energy utilization rate of 82.4% with only 1.4% physical violation rate in synthetic data. The average daily operating cost is reduced to $9,865, outperforming deterministic, stochastic, robust, and reinforcement learning benchmarks by margins of 30.2%, 21.0%, 33.9%, and 15.7%, respectively. Ablation studies confirm that the DRO formulation contributes the largest performance gain, while the trust mechanism effectively incentivizes reliable participant behavior through transparent economic adjustments. The framework maintains feasibility and cost efficiency under renewable penetration levels up to 70%, demonstrating its scalability and robustness for practical VPP operations in highly urbanized power systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1914288</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1914288</link>
        <title><![CDATA[An improved approach for rainfall-runoff simulation under anthropogenic influence]]></title>
        <pubdate>2026-09-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sheng He</author><author>Heting Chen</author><author>Dun Fu</author><author>Shuxin Han</author><author>Zhengzheng Chen</author>
        <description><![CDATA[Human activities significantly influence the rainfall-runoff process, leading to pronounced fluctuations in runoff data and presenting substantial challenges for accurate simulation. In this study, the Water Allocation and Simulation (WAS) model was adopted and enhanced to simulate rainfall-runoff under the impact of human activities. The virtual reservoirs were established within the WAS model to effectively represent water consumption associated with human activities, including agricultural irrigation and pond storage. The model was compared with the results of rainfall-runoff simulation under natural conditions. This study method was applied to simulate rainfall-runoff in the Yanglou hydrological station basin, Northern Anhui Province, China, in which the water cycle is strongly influenced by human activities. The results show that the simulation accuracy of the WAS model is superior to that of rainfall-runoff simulation under natural conditions. The WAS model significantly improved the Nash-Sutcliffe efficiency coefficient (NSE) from 0.704 to 0.898 and the correlation coefficient (R2) improved from 0.869 to 0.952 during the validation period. The WAS model can be practiced in areas with strong human activities. The findings of this study are instrumental in enhancing rainfall-runoff simulation in regions of strong human activities.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1942384</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1942384</link>
        <title><![CDATA[Numerical and experimental analysis of registration of small ammonia impurities using the PLES method]]></title>
        <pubdate>2026-09-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jie Zhang</author><author>Almaz Saifutdinov</author><author>Zijia Chu</author><author>Chen Zhou</author><author>Chengxun Yuan</author><author>Zhongxiang Zhou</author>
        <description><![CDATA[IntroductionThis paper investigates the applicability of detecting ammonia impurities in plasma electron spectroscopy (PLES) through both numerical and experimental approaches. MethodsA self-consistent plasma hybrid fluid-kinetic model is used to accurately describe the formation of characteristic peaks associated with fast electrons in inelastic collision reactions observed in experiments. The model is introduced to calculate the minimum of detectable impurity density at low pressure. ResultsNumerical simulations based on the 1D model predict an idealized theoretical detection limit of approximately 5 ppm for ammonia impurities; however, this value should be regarded as a model-derived theoretical lower bound rather than an experimentally confirmed figure. In practice, factors such as signal-to-noise ratio, probe contamination, and measurement noise make it challenging at this stage to achieve direct and stable detection at such low densities. In addition, the effect of ammonia densities on the electron energy distribution function is studied. At low densities, Penning ionization of metastable helium atoms correlates linearly with ammonia density. This process increases the energy transfer rate and significantly enhances characteristic peak intensities. Conversely, at high densities, metastable species undergo rapid depletion. Frequent inelastic collisions between electrons and ammonia molecules dissipate energy into vibrational modes, ultimately driving the electron energy distribution function into a saturated state dominated by collisional quenching.DiscussionThe results can provide significant reference for the detection of trace impurities in gases based on PLES method.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1891265</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1891265</link>
        <title><![CDATA[DOA estimation for autonomous vehicles by exploiting second-order statistics of Co-array domain of nested arrays by implementing Cuckoo Search Algorithm]]></title>
        <pubdate>2026-09-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zhe Wang</author><author>Ainur Zhetpisbayeva</author><author>Muhammad Salman Qamar</author><author>Berik Zhumazhanov</author><author>Khurram Hameed</author><author>Aliya Kargulova</author>
        <description><![CDATA[Real-world situations require DSRC-based vehicular ad hoc networks that employ sensor fusion and perception systems. Direction of Arrival (DOA) estimation is critical for sensor fusion as it helps track moving objects. This paper presents a new approach to DOA estimation for autonomous vehicles, using a nested sensor array and the Cuckoo Search (CS) Algorithm. A non-uniform nested sensor array, situated on the vehicle, is proposed. The non-uniform array of sensors assists the estimation of DOA with improved angular resolution and is more robust to ambiguities. The CS Algorithm is implemented within the proposed methodology to optimize the iteration for DOA estimation with improved accuracy and speed related to convergence. A thorough comparison of the proposed nested sensor array to a classical sensor array is performed. An example of the CS Algorithm for DOA estimation is presented, along with some real world examples. We demonstrate the power of the algorithm to accurately estimate the direction of incoming signals, even in challenging environments with noise and interference and CS also performs extensive simulations and experimental validation by utilizing the highly non-linear fitness function. This research represents a tremendous advancement in the sensor fusion and DOA estimation for autonomous vehicle research. This presents a much more robust system for perception, aiding autonomous vehicles create a more thorough context when preventing collisions and assisting self driving vehicles aid in safer navigation. The proposed model investigates local and global minima of highly non-linear functions for DOA estimation, and assesses performance using the frequency distribution of RMSE, variability analysis of RMSE, estimation accuracy, RMSE of CDF, and robustness against snapshots and noise and RMSE for Monte Carlo simulation runs.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1937750</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1937750</link>
        <title><![CDATA[Evolutionary dynamics of adaptive multilayer economic networks: a stability-guarded potential game framework]]></title>
        <pubdate>2026-09-28T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yu He</author><author>Zhenhao He</author>
        <description><![CDATA[Complex economic and social systems emerge from repeated strategic interactions on networks whose links also change with behavior. Existing evolutionary-game models commonly assume fixed-interaction structures, whereas adaptive-network approaches often lack a global incentive certificate and an explicit stability condition. This paper develops a co-evolutionary multilayer potential game, in which boundedly rational agents update mixed strategies, while market, information, and institutional links adapt to observed compatibility and diffusion signals. We propose a stability-guarded mirror-replicator (SGMR) dynamic that combines entropy-regularized strategy revision, projected link rewiring, and a spectral safeguard. The mirror step is shown to recover replicator dynamics in the small-step limit. We prove the exact-potential property, monotone potential improvement, sublinear stationarity, and local input-to-state stability under observation disturbances. Computational experiments on synthetic economic networks show faster convergence, higher collective welfare, stronger resilience to noise, and more stable network adaptation than static mirror ascent, standard replicator dynamics, and fictitious play. Additional diagnostics directly evaluate the input-to-state stability bound, quantify the empirical residual decay, and confirm statistically significant welfare gains across network topologies. This framework links evolutionary dynamics, adaptive multilayer networks, and decentralized collective decision-making in complex social systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1917378</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1917378</link>
        <title><![CDATA[On the smallness of the large language model scaling exponents]]></title>
        <pubdate>2026-09-25T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Sauro Succi</author><author>Peter V. Coveney</author><author>Alex Hansen</author>
        <description><![CDATA[We discuss reasons why the scaling exponents of current large language model (LLM) applications are pointing towards a hardly sustainable regime in terms of energy resources. We further show that attributing the smallness of such exponents to a numerical bias due to the neglect of a non-zero value of the loss function in the limit of infinite data (the “pedestal effect”) does not appear to alleviate the sustainability issue. Finally, the effects of the smoothness (roughness) of the data on the scaling exponents are commented upon based on a heuristic analogy with phenomenological models of fluid turbulence.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1902284</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1902284</link>
        <title><![CDATA[Design and application of a portable atmospheric cold plasma jet for food safety and air quality improvement]]></title>
        <pubdate>2026-09-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mohammad Ruzlan Habib</author><author>Sergio Capareda</author><author>Janie Moore</author>
        <description><![CDATA[Non-thermal technologies that can inactivate microorganisms without producing harmful gases, heat damage or chemical residues can contribute largely to food safety and environmental applications. This study reports the design, preliminary characterization, and application of a portable atmospheric cold plasma (ACP) jet intended for local food surface decontamination and indoor air pollutant degradation. The device was constructed with a dielectric-barrier configuration (6.58 mm electrode gap) and operated using filtered ambient air at 1.1 L/min. Electrical behavior was assessed via oscilloscope measurements, while reactive gas species (RGS) were evaluated using optical emission spectroscopy (OES) and colorimetric quantification of ozone and NOx. Additionally, methylene blue (MB) discoloration was performed to confirm oxidative activity and functional validation of the reactor. The plasma jet demonstrated peak-to-peak voltage and current reaching up to 11.8 kV and 87 mA respectively. The produced mean ozone and NOx concentrations were up to 50 ppm and 41.5 ppm, respectively, depending on operating voltage. Plasma treatment at 4.5 kV for 5 min numerically delayed Aspergillus flavus (A. flavus) growth by up to 4 days under refrigerated storage and reduced colony counts relative to controls. For the volatile organic compounds (VOC) treatment, maximum reductions of 92.17% (formaldehyde (HCHO)) and 91.89% (Total VOC or TVOC) were achieved at 4.5 kV after 38 min. Overall, the proposed handheld ACP jet demonstrated the feasibility of localized fungal growth suppression and VOC reduction while highlighting areas requiring further optimization.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1971273</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1971273</link>
        <title><![CDATA[Editorial: Promoting green computing in high-energy physics and astrophysics]]></title>
        <pubdate>2026-09-24T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Vasiliki A. Mitsou</author><author>Andreas Redelbach</author><author>Eleni Vardoulaki</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1938106</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1938106</link>
        <title><![CDATA[Multi-disease transmission with coupling and threshold perturbations on hypergraph structure]]></title>
        <pubdate>2026-09-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xie Yang</author><author>Jun Yin</author><author>Xiujuan Ma</author><author>Luqian Wang</author>
        <description><![CDATA[In reality, infectious diseases rarely spread in isolation; instead, multiple diseases often spread concurrently. The infection of one disease may influence an individual’s susceptibility or transmissibility of another disease through mechanisms such as immunosuppression or symptom superposition. Furthermore, population contact structures are not limited to simple pairwise interactions, but also involve group events with simultaneous exposure, such as family gatherings. Traditional network models, which are based on pairwise interactions, are difficult to accurately capture these higher-order interaction structures and the coupling mechanisms among multiple pathogens. Therefore, this paper presents a hypergraph SIS transmission model based on dynamic thresholds. It systematically investigates the transmission dynamics of multiple diseases on uniform and non-uniform hypergraph structures in BA and ER networks. According to co-infection scenarios, three coupling mechanisms are proposed: positive coupling which promotes co-infection, negative coupling which suppresses co-infection, and no coupling where transmission occurs independently. To account for variations in initial disease intensity, three comparison groups are designed: high-low, same-high and same-low. This paper analyses the combined effects of coupling mechanisms, threshold variations and network structural characteristics on the transmission thresholds, propagation rates and infection scales of multiple diseases. These findings are validated in a dengue fever transmission network.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1937180</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1937180</link>
        <title><![CDATA[Channel membership mechanisms in UGC platforms: content creation, revenue structure, and platform incentives]]></title>
        <pubdate>2026-09-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Liang Yongtao</author><author>Ma Zhiqiang</author>
        <description><![CDATA[Channel membership has gradually become an important monetization mechanism for both UGC platforms and content creators. However, channel membership may also reshape creators’ allocation of effort, thereby affecting content supply, monetization strategies, and platform profitability. To examine the implications of channel membership for the sustainability of UGC platform ecosystems, this study develops a three-stage game-theoretic model involving the platform, content creators, and consumers, and compares two representative channel membership mechanisms: content-partition membership, in which certain content remains permanently exclusive to members, and time-partition membership, in which members receive temporary early access before the content becomes freely available. The results show that UGC platforms exhibit interior optimal commission rates on both the advertising and subscription sides. Moreover, channel membership does not necessarily reduce the quality of free content; its effects depend on the membership mechanism and market conditions. Content-partition membership is more suitable for environments characterized by strong user willingness to pay and high-value content, whereas time-partition membership is better suited to platforms where advertising revenue plays a more important role. These findings provide useful insights for the design of channel membership mechanisms and monetization strategies on UGC platforms.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1854077</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1854077</link>
        <title><![CDATA[Boost or burden? The nonlinear impact of artificial intelligence on innovation resilience within a dynamic capabilities framework: evidence from Chinese listed companies]]></title>
        <pubdate>2026-09-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xiaoyan Wang</author><author>Xiangyu Li</author><author>Yanan He</author>
        <description><![CDATA[This research explores the effect of artificial intelligence on innovation resilience. This paper uses Chinese A-share listed companies from 2010 to 2024 as its research sample and employs a two-way fixed-effects panel regression model to empirically test the research hypotheses. The results show that AI and innovation resilience exhibit an inverted U-shaped relationship, where the impact initially increases before diminishing. The mediating mechanism indicates that AI affects innovation resilience through corporate learning and absorption capacity, coordination and integration capacity, and technological innovation capacity. The result of the moderating mechanism indicates that the association is positively moderated by environmental dynamism. Findings from the heterogeneity analysis indicate that the effect of AI on innovation resilience is more notable in state-owned companies, high-tech companies, and large-scale companies. Further analysis reveals that enhanced innovation resilience contributes to promoting high-quality corporate development. These findings provide theoretical insights and policy recommendations for advancing AI adoption and strengthening corporate innovation resilience.]]></description>
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