Frontiers in Physics

An information-theory examination of the “upstream-turbulence effect” in solar-wind/magnetosphere coupling

2026-04-02

Simon Wing, Joseph E. Borovsky

IntroductionCorrelations between upstream solar-wind turbulence—measured by the amplitude of magnetic-field fluctuations (ΔB/B)—and increases in geomagnetic activity have been reported by many research groups. However, questions remain as to whether this relationship reflects a true cause-and-effect interaction. This study investigates whether solar-wind turbulence exerts a causal influence on the magnetosphere.MethodsTransfer entropy (TE) was used as a quantitative measure of causality to evaluate information transfer from ΔB/B to geomagnetic indices AE and Dst. The magnitude and lag time (τ) of information transfer were compared with those derived from solar wind velocity (Vsw) and from reconnection-driven Rquick and Newell solar wind coupling functions.ResultsThe analysis shows that ΔB/B exerts a real but weak causal influence on the magnetosphere, operating over relatively long time scales. Information transfer from ΔB/B to AE and Dst is approximately an order of magnitude smaller and peaks at larger lag times than transfers from Rquick and Newell solar wind coupling functions. In contrast, the lag times for information transfer from ΔB/B and Vsw to AE and Dst are comparable. The information transfer from ΔB/B to AE is approximately 45% of that from Vsw, while the transfer from ΔB/B to Dst is about an order of magnitude smaller than that from Vsw.DiscussionThe similarity in response times between ΔB/B and Vsw suggests that turbulence interacts with the magnetosphere primarily through viscous-like processes rather than reconnection-driven mechanisms. Although both ΔB/B and Vsw influence the magnetosphere through viscous interactions, ΔB/B exhibits lower geoeffectiveness. These findings indicate that solar wind turbulence affects different magnetospheric regions and phenomena to varying degrees and confirm that its influence, while real, is relatively weak.

Underwater 3D sound speed field reconstruction based on block term tensor decomposition

2026-03-30

Hao Wu, Maofa Wang, Jiabao Zhao

The three-dimensional sound speed field (SSF) is of great significance in underwater acoustic research; however, the high cost of maritime observation often leads to sparse and limited measurement data, making accurate SSF reconstruction a challenging yet valuable problem. Traditional inversion methods frequently suffer from high data requirements, an inability to process complex spatiotemporal features, and issues regarding accuracy and stability. To address these challenges, this paper proposes a 3D SSF reconstruction method that combines Block Term Tensor Decomposition (BTD) with sparse reconstruction. Specifically, BTD is utilized to extract latent information and structural features from high-dimensional historical ocean sound speed data, enabling precise 3D SSF reconstruction by integrating a small number of newly acquired regional observations through sparse reconstruction techniques. The proposed method was validated using the Argo dataset and experimental sea trial data from underwater gliders. Experimental results demonstrate that when reconstructing with limited observations, the BTD-based approach improves reconstruction accuracy by more than 49.25% compared to traditional Empirical Orthogonal Function and Tucker decomposition methods. Overall, utilizing BTD for 3D underwater sound speed field reconstruction represents a novel, high-precision, and cost-effective methodology that effectively overcomes data sparsity constraints.

Inversion of the forest dead fuel moisture content by UAV multisepectral image under the new leaves shade

2026-03-26

Ye Wang, Xinning Wang, Jian Xing

Forest dead fuel moisture content (FDFMC) is an important factor affecting the occurrence and spread of forest fires. When the leaves have completely fallen, because of no leaves shade, the use of UAV multispectral cameras can achieve the spectral images easily. However, during the spring fire prevention period, it is difficult to obtain the full spectral images because of the shade of new leaves, therefore the inversion accuracy of FDFMC would be greatly affected by it. In this paper, an improved ConvNeXt convolutional neural network is proposed to predict FDFMC based on UAV multispectral camera data from 18 to 25 April 2025 in the urban forestry demonstration in Harbin City. A total of 6,031 sets of photos were captured using UAV multispectral camera, with each set containing six single-band images. The K-means clustering algorithm is used to segment the UAV multispectral images to extract the feature information for reducing the influence of new leaves shade. The trained model achieved 1.38% for MAE and 4.54% for RMSE. The experimental results showed that the improved ConvNeXt model can accurately predict the FDFMC. The new method proposed in this paper for predicting the FDFMC using the UAV multispectral images has feasibility and reference significance.

Hyper-S2IR: a model for characterizing higher-order interactions and dynamics in public opinion dissemination

2026-03-26

Chunying Zhang, Xiangyu Li, Lu Liu, Jing Ren, Jiang Ma, Liyan Zhang

Traditional public opinion diffusion models generally assume interactions between individuals as binary pair-wise effects, which struggle to capture the higher-order complexities of multi-group interactions in social networks—such as group discussions in WeChat and topic reposting on Weibo. Moreover, these models fail to adequately depict the nonlinear trust accumulation mechanisms and individual heterogeneity inherent in the diffusion process. Therefore, the paper proposes a hypergraph-based Hyper-S2IR model for disseminating public opinion. The “Goebbels effect” is operationalized by leveraging the hypergraph structure: a susceptible node’s risk of infection is proportional to its hyperdegree, mathematically representing the cumulative exposure to information from multiple sources within different hyperedges. Our model introduces two types of communicators (HI1 and HI2) with different motivations and capabilities, thereby systematically depicting the inherent heterogeneity of the communication group. Through theoretical derivation, we derive a novel basic reproduction number R0 that explicitly incorporates the hyperdegree distribution of the hypergraph. This R0 provides a threshold for dissemination dynamics: When R0 > 1, the public opinion will continue to spread and converge to a stable public opinion prevalence equilibrium point; when R0 < 1, the public opinion will gradually disappear. Critically, the expression for R0 reveals how higher-order group interactions, encoded in the hyperdegree, fundamentally alter the spreading threshold compared to traditional pairwise networks. Numerical simulations verify the theoretical conclusions and demonstrate that the hypergraph structure significantly accelerates the spread and expands the scale of public opinion compared to traditional network structures. This work provides theoretical support and a quantitative basis for analyzing public opinion dissemination mechanisms and formulating intervention strategies.

The R&D game of technological achievements in industrial parks under the “administrative committee + enterprise” model: a CPSS perspective

2026-03-25

Meng Qiu, Haitao Ji, Miao Wang, Jifa Wang

Under the background of digitalization and collaborative innovation, high-tech industrial parks have gradually evolved into complex innovation systems characterized by multi-agent interaction. From the perspective of social physics, this thesis constructs a tripartite evolutionary game model involving high-tech industrial parks under the “administrative committee + enterprise” mode, park technology enterprises, and academic research institutions within the CPSS (Cyber-Physical-Social System) framework, and analyzes the influence mechanisms of key parameters in the information layer, physical layer, and social layer on system stability through replicator dynamic equations and numerical simulation. The results indicate that cooperative R&amp;D of scientific and technological achievements is not a linear process of input accumulation, but a nonlinear evolutionary system driven by cross-layer coupling relationships. The simulation results show that the level of value-added services, as the support intensity of the information layer, exhibits a significant threshold effect. Moderate information support can reduce information asymmetry and promote system convergence toward a collaborative equilibrium, whereas excessively low or excessively high levels may lead to system instability. Investment shareholding changes the stability interval of the system by influencing resource allocation and benefit distribution structures. Appropriate participation contributes to the formation of a risk-sharing mechanism, while excessive shareholding weakens enterprises’ incentives for R&amp;D. The subsidy ratio reflects governance intensity in the social layer. Moderate intervention can promote the formation of collaboration, whereas excessive intervention may cause strategic distortion and system disturbance. Strategic returns constitute a key variable driving the system from a non-cooperative state to a stable collaborative equilibrium. When system-level collaborative benefits exceed a critical threshold, a cross-layer positive feedback mechanism emerges.

Correction: 10 kHz repetition rate picosecond green laser for high-accuracy satellite ranging

2026-03-23

Zhongwei Fan, Xiaopeng Liu, Zhongping Zhang, Wendong Meng, Mingliang Long, Zhenao Bai