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The discovery of 2-dimensional (2D) materials, such as CrI 3 , that retain magnetic ordering at monolayer thickness has resulted in a surge of both pure and applied research in 2D magnetism. Here, we report a magneto-Raman spectroscopy study on multilayered CrI 3 , focusing on two additional features in the spectra that appear below the magnetic ordering temperature and were previously assigned to high frequency magnons. Instead, we conclude these modes are actually zone-folded phonons. We observe a striking evolution of the Raman spectra with increasing magnetic field applied perpendicular to the atomic layers in which clear, sudden changes in intensities of the modes are attributed to the interlayer ordering changing from antiferromagnetic to ferromagnetic at a critical magnetic field. Our work highlights the sensitivity of the Raman modes to weak interlayer spin ordering in CrI 3 . Thin samples CrI 3 exhibit a phase transition under an applied magnetic field from layered antiferromagnetism to ferromagnetism. Here the authors observe an associated abrupt change in the magneto-Raman spectra, illustrating the sensitivity of Raman spectra to magnetic ordering.

With the rapid advancement of speech synthesis and voice conversion technologies, audio deepfake techniques have posed serious threats to information security. Existing detection methods often lack robustness when confronted with environmental noise, signal compression, and ambiguous fake features, making it difficult to effectively identify highly concealed fake audio. To address this issue, this paper proposes a Dual-Path Time-Frequency Attention Network (DPTFAN) based on Pythagorean Hesitant Fuzzy Sets (PHFS), which dynamically characterizes the reliability and ambiguity of fake features through uncertainty modeling. It introduces a dual-path attention mechanism in both time and frequency domains to enhance feature representation and discriminative capability. Additionally, a Lightweight Fuzzy Branch Network (LFBN) is designed to achieve explicit enhancement of ambiguous features, improving performance while maintaining computational efficiency. On the ASVspoof 2019 LA dataset, the proposed method achieves an accuracy of 98.94%, and on the FoR (Fake or Real) dataset, it reaches an accuracy of 99.40%, significantly outperforming existing mainstream methods and demonstrating excellent detection performance and robustness.

Pine Wilt Disease (PWD) is a highly infectious and lethal disease that severely threatens global pine forest ecosystems and forestry economies. Early and accurate detection of infected trees is crucial to prevent large-scale outbreaks and support timely forest management. However, existing remote sensing-based detection models often struggle with performance degradation in complex environments, as well as a trade-off between detection accuracy and real-time efficiency. To address these challenges, we propose an improved object detection model, YOLOv8-MFD, designed for accurate and efficient detection of PWD-infected trees from UAV imagery. The model incorporates a MobileViT-based backbone that fuses convolutional neural networks with Transformer-based global modeling to enhance feature representation under complex forest backgrounds. To further improve robustness and precision, we integrate a Focal Modulation mechanism to suppress environmental interference and adopt a Dynamic Head to strengthen multi-scale object perception and adaptive feature fusion. Experimental results on a UAV-based forest dataset demonstrate that YOLOv8-MFD achieves a precision of 92.5%, a recall of 84.7%, an F1-score of 88.4%, and a mAP@0.5 of 88.2%. Compared to baseline models such as YOLOv8 and YOLOv10, our method achieves higher accuracy while maintaining acceptable computational cost (11.8 GFLOPs) and a compact model size (10.2 MB). Its inference speed is moderate and still suitable for real-time deployment. Overall, the proposed method offers a reliable solution for early-stage PWD monitoring across large forested areas, enabling more timely disease intervention and resource protection. Furthermore, its generalizable architecture holds promise for broader applications in forest health monitoring and agricultural disease detection.

A bstract The dynamics of quantum entanglement plays a central role in explaining the emergence of thermal equilibrium in isolated many-body systems. However, entanglement is notoriously hard to measure, and can in fact be “forged”: recent works have introduced a notion of pseudoentanglement describing ensembles of many-body states that, while only weakly entangled, cannot be efficiently distinguished from states with much higher entanglement, such as random states in the Hilbert space. This prompts the question: how much entanglement is truly necessary to achieve thermal equilibrium in quantum systems? In this work we address this question by introducing random circuit models of quantum dynamics that, at late times, equilibrate to pseudoentangled ensembles — a phenomenon we name ensemble pseudothermalization . These models replicate all the efficiently observable predictions of thermal equilibrium, while generating only a small (and tunable) amount of entanglement, thus deviating from the “maximum-entropy principle” that underpins thermodynamics. We examine (i) how a pseudoentangled ensemble on a small subsystem spreads to the whole system as a function of time, and (ii) how a pseudoentangled ensemble can be generated from an initial product state. We map the above problems onto a family of classical Markov chains on subsets of the computational basis. The mixing times of such Markov chains are related to the time scales at which the states produced from the dynamics become indistinguishable from Haar-random states at the level of each statistical moment, or number of copies. Based on a combination of rigorous bounds and conjectures supported by numerics, we argue that each Markov chain’s relaxation time and mixing time have different asymptotic behavior in the limit of large system size. This is a necessary condition for a cutoff phenomenon : an abrupt dynamical transition to equilibrium. We thus conjecture that our random circuits give rise to asymptotically sharp distinguishability transitions.

To address the complex challenge of recognizing mixed handwritten text in practical scenarios such as examination papers and to overcome the limitations of existing methods that typically focus on a single category, this paper proposes MHTR, a Multimodal Handwritten Text Adaptive Recognition algorithm. The framework comprises two key components, a Handwritten Character Classification Module and a Handwritten Text Adaptive Recognition Module, which work in conjunction. The classification module performs fine-grained analysis of the input image, identifying different types of handwritten content such as Chinese characters, digits, and mathematical formula. Based on these results, the recognition module dynamically selects specialized sub-networks tailored to each category, thereby enhancing recognition accuracy. To further reduce errors caused by similar character shapes and diverse handwriting styles, a Context-aware Recognition Optimization Module is introduced. This module captures local semantic and structural information, improving the model’s understanding of character sequences and boosting recognition performance. Recognizing the limitations of existing public handwriting datasets, particularly their lack of diversity in character categories and writing styles, this study constructs a heterogeneous, integrated handwritten text dataset. The dataset combines samples from multiple sources, including Chinese characters, numerals, and mathematical symbols, and features high structural complexity and stylistic variation to better reflect real-world application needs. Experimental results show that MHTR achieves a recognition accuracy of 86.63% on the constructed dataset, significantly outperforming existing methods. Furthermore, the context-aware optimization module demonstrates strong adaptive correction capabilities in various misrecognition scenarios, confirming the effectiveness and practicality of the proposed approach for complex, multi-category handwritten text recognition tasks.

This paper introduces a class of insulin-glucose-glucocorticoid impulsive systems in the treatment of patients with diabetes to consider the effect of glucocorticoids. The existence and uniqueness of the positive periodic solution of the impulsive model at double fixed time is confirmed for type 1 diabetes mellitus (T1DM) using the function. Further, the global asymptotic stability of the positive periodic solution is achieved following Floquet multiplier theory and comparison principle. Additionally, the permanence of the system is confirmed in type 2 diabetes mellitus (T2DM) via the comparison theorem. Numerical analysis verifies the results of theoretical calculations and indicates that combining therapeutic strategies under hormonal interactions with the dose of exogenous insulin and glucocorticoid medicines within organisms provides more reasonable clinical strategies.

Maladaptive changes of nerve injury-associated genes in dorsal root ganglia (DRGs) are critical for neuropathic pain genesis. Emerging evidence supports the role of long noncoding RNAs (lncRNAs) in regulating gene transcription. Here we identified a conserved lncRNA, named nerve injury-specific lncRNA (NIS-lncRNA) for its upregulation in injured DRGs exclusively in response to nerve injury. This upregulation was triggered by nerve injury-induced increase in DRG ELF1, a transcription factor that bound to the NIS-lncRNA promoter. Blocking this upregulation attenuated nerve injury-induced CCL2 increase in injured DRGs and nociceptive hypersensitivity during the development and maintenance periods of neuropathic pain. Mimicking NIS-lncRNA upregulation elevated CCL2 expression, increased CCL2-mediated excitability in DRG neurons, and produced neuropathic pain symptoms. Mechanistically, NIS-lncRNA recruited more binding of the RNA-interacting protein FUS to the Ccl2 promoter and augmented Ccl2 transcription in injured DRGs. Thus, NIS-lncRNA participates in neuropathic pain likely by promoting FUS-triggered DRG Ccl2 expression and may be a potential target in neuropathic pain management.

Pine wilt disease, a highly contagious forest disease caused by the pine wood nematode and primarily transmitted via its insect vector, the pine sawyer beetle (Monochamus spp.), poses a significant threat to forest ecosystems. Accurate detection of infected trees is vital for effective prevention and control. This study pioneers the detection of pine wilt disease-infected trees in the China's Qinba Mountain region, where the complex terrain and uneven forest distribution thinder feature extraction of diseased trees. To address data collection challenge, this paper proposes a novel feature recognition and detection method for pine wilt disease-infected trees based on an FLMP-YOLOv8 algorithm. The enhanced features include: first, integrating FasterBlock module into the backbone and neck networks of YOLOv8 to, boost the model's feature extraction capability and reduce complexity, thereby achieving a balance between detection efficiency and accuracy. Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model's ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. Experimental results on a self-constructed dataset demonstrate the improved model efficacy, achieving 92.0% precision, 80.8% recall, 87.0% mean Average Precision (mAP@0.5), and 81.79 FPS detection speed. Compared to the original YOLOv8 model, the improved algorithm shows increases of 2.2% in precision, 0.6% in recall, and 2.0% in mAP@0.5, with a detection speed improvement of 65.48 FPS. This study provides a more reliable and cost-effective method for detecting trees infected with pine wilt disease.

In this paper, based on the classical HR neuron model, a double time-delay flux neuron model with magnetron memristor is proposed in this paper. The stability, the existence of Hopf bifurcation, direction of bifurcation and bifurcation cycle solution of the model are studied using Routh Hurwitz judgment method and central manifold theorem. As a result, we prove that there exists a bifurcation periodic solution in the specific time-delay range of the model, and the change law of spike discharge behavior induced by increasing double time-delay is clarified, which is helpful to explain the abnormal discharge behavior of brain or nerve center caused by electromagnetic radiation. The time-series diagram and phase diagram of the model under different time lag are obtained by numerical simulation with MATLAB software. The simulation result shows that under appropriate conditions, the time-delay is less than a critical value, and the system is asymptotically stable. When the time-delay exceeds the critical value, Hopf bifurcation occurs at the equilibrium point of the system.

Pine wilt disease (PWD), also known as pine wilt nematode disease, is a severe forest disease caused by the pine wood nematode (Bursaphelenchus xylophilus), which spreads rapidly and causes severe ecological damage in China. However, the existing detection methods have low accuracy in identifying early‐stage infected trees and are prone to missing detections. To address these issues, this study proposes YOLOv11‐OC, a detection method of pine wilt disease based on improved YOLOv11 with UAV remote sensing images. The omni‐dimensional dynamic convolution (ODConv) is employed to optimize the C3K2 module, thereby enhancing the feature extraction ability for small targets and improving the accuracy of identifying early‐stage disease areas. Meanwhile, the context anchor attention (CAA) mechanism is introduced to improve the C2PSA module, enhancing the model's context awareness, effectively reducing missed detections, and improving detection performance in complex backgrounds. Experimental results demonstrate that the proposed YOLOv11‐OC algorithm outperformed the original YOLOv11, achieving a precision of 94.2%, a recall of 83.1%, a F1‐score of 88.3%, and a mean average precision (mAP) of 88.9%. Compared to the original algorithm, the improved version shows increases of 2.8% in precision, 1.0% in F1‐score, and 1.4% in mAP. Furthermore, the algorithm also demonstrates good generalization ability on the public PlantDoc dataset. The proposed YOLOv11‐OC model enhances detection performance in PWD‐infected trees in two main ways. On one hand, the omni‐dimensional dynamic convolution (ODConv) module improves the C3K2 by using a multi‐dimensional attention mechanism to adaptively adjust the convolution kernel weights, thereby enhancing the model's ability to extract features from small targets. On the other hand, the context anchor attention (CAA) module is introduced to improve the C2PSA module, boosting the model's ability to capture long‐range features of PWD‐infected trees, which reduces missed detections. These improvements further enhance detection accuracy, particularly in challenging scenarios involving branch occlusion, complex tree morphology, and environmental interference.