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21 pages, 360 KB  
Article
The Symmetry of Interdependence in Human–AI Teams and the Limits of Classical Team Science
by William Lawless
AppliedMath 2025, 5(3), 114; https://doi.org/10.3390/appliedmath5030114 - 1 Sep 2025
Viewed by 460
Abstract
Our research goal is to provide the mathematical guidance to enable any combination of “intelligent” machines, artificial intelligence (AI) and humans to be able to interact with each other in roles that form the structure of a team interdependently performing a team’s tasks. [...] Read more.
Our research goal is to provide the mathematical guidance to enable any combination of “intelligent” machines, artificial intelligence (AI) and humans to be able to interact with each other in roles that form the structure of a team interdependently performing a team’s tasks. Our quantum-like model, representing one of the few, if only, mathematical models of interdependence, captures the tradeoffs in energy expenditures a team chooses as it consumes its available energy on its structure versus its performance, measured by the uncertainty (entropy) relationship generated. Here, we outline the support for our quantum-like model of uncertainty relations, our goals in this study, and our future plans: (i) Redundancy reduces interdependence. This first finding confirms the existence of interdependence in systems, both large and small. (ii) Teams with orthogonal roles perform best. This second finding is the root cause of humans, including scientists, being unable to appreciate the role of interdependence in “squeezing” states of teams. (iii) Cognitive reports may not equal behavior. The last finding allows us to tie our research together and to account for the absence of social scientists from leading the mathematical science of teams. In this article, we review the need for a mathematics for the future of team operations, the literature, the mathematics in our model of agents with full agency (viz., intelligent and interdependent), our hypothesis that freely organized teams enjoy significant advantages over command decision-making (CDM) systems, and results from the field. We close with future plans and a generalization about squeezing states to control interdependent systems. Full article
19 pages, 1479 KB  
Article
Ada-DF++: A Dual-Branch Adaptive Facial Expression Recognition Method Integrating Global-Aware Spatial Attention and Squeeze-and-Excitation Attention
by Zhi-Rui Li, Zheng-Jie Deng, Xi-Yan Li, Wei-Dong Ke, Si-Jian Yan, Jun-Du Zhang and Chang Liu
Sensors 2025, 25(17), 5258; https://doi.org/10.3390/s25175258 - 24 Aug 2025
Viewed by 872
Abstract
Facial Expression Recognition (FER) is a research topic of great practical significance. However, existing FER methods still face numerous challenges, particularly in the interaction between spatial and global information, the distinction of subtle expression features, and the attention to key facial regions. This [...] Read more.
Facial Expression Recognition (FER) is a research topic of great practical significance. However, existing FER methods still face numerous challenges, particularly in the interaction between spatial and global information, the distinction of subtle expression features, and the attention to key facial regions. This paper proposes a lightweight Global-Aware Spatial (GAS) Attention module, designed to improve the accuracy and robustness of FER. This module extracts global semantic information from the image via global average pooling and fuses it with local spatial features extracted by convolution, guiding the model to focus on regions highly relevant to facial expressions (such as the mouth and eyes). This effectively suppresses background noise and enhances the model’s ability to perceive subtle expression variations. In addition, we further introduce a Squeeze-and-Excitation (SE) Attention module into the dual-branch architecture to adaptively adjust the channel-wise weights of features, emphasizing critical region information and enhancing the model’s discriminative capacity. Based on these improvements, we develop the Ada-DF++ network model. Experimental results show that the improved model achieves test accuracies of 89.21%, 66.14%, and 63.75% on the RAF-DB, AffectNet (7cls), and AffectNet (8cls) datasets, respectively, outperforming current state-of-the-art methods across multiple benchmarks and demonstrating the effectiveness of the proposed approach for FER tasks. Full article
(This article belongs to the Section Intelligent Sensors)
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24 pages, 1471 KB  
Article
WDM-UNet: A Wavelet-Deformable Gated Fusion Network for Multi-Scale Retinal Vessel Segmentation
by Xinlong Li and Hang Zhou
Sensors 2025, 25(15), 4840; https://doi.org/10.3390/s25154840 - 6 Aug 2025
Viewed by 649
Abstract
Retinal vessel segmentation in fundus images is critical for diagnosing microvascular and ophthalmologic diseases. However, the task remains challenging due to significant vessel width variation and low vessel-to-background contrast. To address these limitations, we propose WDM-UNet, a novel spatial-wavelet dual-domain fusion architecture that [...] Read more.
Retinal vessel segmentation in fundus images is critical for diagnosing microvascular and ophthalmologic diseases. However, the task remains challenging due to significant vessel width variation and low vessel-to-background contrast. To address these limitations, we propose WDM-UNet, a novel spatial-wavelet dual-domain fusion architecture that integrates spatial and wavelet-domain representations to simultaneously enhance the local detail and global context. The encoder combines a Deformable Convolution Encoder (DCE), which adaptively models complex vascular structures through dynamic receptive fields, and a Wavelet Convolution Encoder (WCE), which captures the semantic and structural contexts through low-frequency components and hierarchical wavelet convolution. These features are further refined by a Gated Fusion Transformer (GFT), which employs gated attention to enhance multi-scale feature integration. In the decoder, depthwise separable convolutions are used to reduce the computational overhead without compromising the representational capacity. To preserve fine structural details and facilitate contextual information flow across layers, the model incorporates skip connections with a hierarchical fusion strategy, enabling the effective integration of shallow and deep features. We evaluated WDM-UNet in three public datasets: DRIVE, STARE, and CHASE_DB1. The quantitative evaluations demonstrate that WDM-UNet consistently outperforms state-of-the-art methods, achieving 96.92% accuracy, 83.61% sensitivity, and an 82.87% F1-score in the DRIVE dataset, with superior performance across all the benchmark datasets in both segmentation accuracy and robustness, particularly in complex vascular scenarios. Full article
(This article belongs to the Section Sensing and Imaging)
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19 pages, 5417 KB  
Article
SE-TFF: Adaptive Tourism-Flow Forecasting Under Sparse and Heterogeneous Data via Multi-Scale SE-Net
by Jinyuan Zhang, Tao Cui and Peng He
Appl. Sci. 2025, 15(15), 8189; https://doi.org/10.3390/app15158189 - 23 Jul 2025
Viewed by 480
Abstract
Accurate and timely forecasting of cross-regional tourist flows is essential for sustainable destination management, yet existing models struggle with sparse data, complex spatiotemporal interactions, and limited interpretability. This paper presents SE-TFF, a multi-scale tourism-flow forecasting framework that couples a Squeeze-and-Excitation (SE) network with [...] Read more.
Accurate and timely forecasting of cross-regional tourist flows is essential for sustainable destination management, yet existing models struggle with sparse data, complex spatiotemporal interactions, and limited interpretability. This paper presents SE-TFF, a multi-scale tourism-flow forecasting framework that couples a Squeeze-and-Excitation (SE) network with reinforcement-driven optimization to adaptively re-weight environmental, economic, and social features. A benchmark dataset of 17.8 million records from 64 countries and 743 cities (2016–2024) is compiled from the Open Travel Data repository in github (OPTD) for training and validation. SE-TFF introduces (i) a multi-channel SE module for fine-grained feature selection under heterogeneous conditions, (ii) a Top-K attention filter to preserve salient context in highly sparse matrices, and (iii) a Double-DQN layer that dynamically balances prediction objectives. Experimental results show SE-TFF attains 56.5% MAE and 65.6% RMSE reductions over the best baseline (ARIMAX) at 20% sparsity, with 0.92 × 103 average MAE across multi-task outputs. SHAP analysis ranks climate anomalies, tourism revenue, and employment as dominant predictors. These gains demonstrate SE-TFF’s ability to deliver real-time, interpretable forecasts for data-limited destinations. Future work will incorporate real-time social media signals and larger multimodal datasets to enhance generalizability. Full article
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10 pages, 347 KB  
Article
The Evolution of Squeezing in Coupled Macroscopic Mechanical Oscillator Systems
by Xin-Chang Liu, Xiao-Dong Shi, Xiao-Lei Zhang, Ling-Xiao Chen and Yi Zhang
Electronics 2025, 14(14), 2817; https://doi.org/10.3390/electronics14142817 - 13 Jul 2025
Viewed by 463
Abstract
Quantum squeezing in macroscopic oscillator systems plays a critical role in bridging quantum mechanics with classical-scale phenomena, enabling high-precision measurements and fundamental tests of quantum physics. In this work, we investigate the effect of squeezing on the phonon state in a hybrid macroscopic [...] Read more.
Quantum squeezing in macroscopic oscillator systems plays a critical role in bridging quantum mechanics with classical-scale phenomena, enabling high-precision measurements and fundamental tests of quantum physics. In this work, we investigate the effect of squeezing on the phonon state in a hybrid macroscopic mechanical system consisting of an ensemble of Rydberg atoms coupled to two macroscopic mechanical oscillators. We notice that the dipole–dipole coupling between atoms and mechanical oscillators can be transferred to the indirectly coupled mechanical interaction, and the nonlinear effective Hamiltonian can be solved to generate a squeezed effect on the mechanical mode. We also discuss the noise effects induced by amplitude and phase fluctuations on the squeezed quadratures of the system. Full article
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23 pages, 9338 KB  
Article
Numerical Investigation of the Tribological Performance of Surface-Textured Bushings in External Gear Pumps Under Transient Lubrication Conditions
by Paolo Casoli, Masoud Hatami Garousi, Massimo Rundo and Carlo Maria Vescovini
Actuators 2025, 14(7), 345; https://doi.org/10.3390/act14070345 - 11 Jul 2025
Viewed by 402
Abstract
This study presents a computational fluid dynamics (CFDs) investigation of the hydrodynamic behavior of surface-textured lateral bushings in external gear pumps (EGPs), emphasizing the effects of combined sliding and squeezing motions within the lubrication gap. A comprehensive numerical model was developed to analyze [...] Read more.
This study presents a computational fluid dynamics (CFDs) investigation of the hydrodynamic behavior of surface-textured lateral bushings in external gear pumps (EGPs), emphasizing the effects of combined sliding and squeezing motions within the lubrication gap. A comprehensive numerical model was developed to analyze how surface texturing implemented through different dimple shapes and texture densities influences pressure distribution and load-carrying capacity under transient lubrication conditions. The analysis demonstrates that the interaction between shear-driven flow and squeeze-film compression significantly amplifies pressure, particularly when optimal dimple configurations are applied. Results indicate that dimple geometry, depth, and arrangement critically influence hydrodynamic performance, while excessive texturing reduces effectiveness due to increased average gap height. Cavitation was intentionally not modeled in the early single dimple evaluations to allow clear comparison between configurations. The findings offer a design guideline for employing surface textures to enhance tribological performance and efficiency in EGP applications under realistic dynamic conditions. Full article
(This article belongs to the Special Issue Advances in Fluid Power Systems and Actuators)
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21 pages, 2471 KB  
Article
Attention-Based Mask R-CNN Enhancement for Infrared Image Target Segmentation
by Liang Wang and Kan Ren
Symmetry 2025, 17(7), 1099; https://doi.org/10.3390/sym17071099 - 9 Jul 2025
Viewed by 970
Abstract
Image segmentation is an important method in the field of image processing, while infrared (IR) image segmentation is one of the challenges in this field due to the unique characteristics of IR data. Infrared imaging utilizes the infrared radiation emitted by objects to [...] Read more.
Image segmentation is an important method in the field of image processing, while infrared (IR) image segmentation is one of the challenges in this field due to the unique characteristics of IR data. Infrared imaging utilizes the infrared radiation emitted by objects to produce images, which can supplement the performance of visible-light images under adverse lighting conditions to some extent. However, the low spatial resolution and limited texture details in IR images hinder the achievement of high-precision segmentation. To address these issues, an attention mechanism based on symmetrical cross-channel interaction—motivated by symmetry principles in computer vision—was integrated into a Mask Region-Based Convolutional Neural Network (Mask R-CNN) framework. A Bottleneck-enhanced Squeeze-and-Attention (BNSA) module was incorporated into the backbone network, and novel loss functions were designed for both the bounding box (Bbox) regression and mask prediction branches to enhance segmentation performance. Furthermore, a dedicated infrared image dataset was constructed to validate the proposed method. The experimental results demonstrate that the optimized model achieves higher segmentation accuracy and better segmentation performance compared to the original network and other mainstream segmentation models on our dataset, demonstrating how symmetrical design principles can effectively improve complex vision tasks. Full article
(This article belongs to the Special Issue Symmetry and Its Applications in Computer Vision)
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21 pages, 5274 KB  
Article
Drive-Loss Engineering and Quantum Discord Probing of Synchronized Optomechanical Squeezing
by Hugo Molinares and Vitalie Eremeev
Mathematics 2025, 13(13), 2171; https://doi.org/10.3390/math13132171 - 3 Jul 2025
Viewed by 412
Abstract
In an optomechanical system (OMS), the dynamics of quantum correlations, e.g., quantum discord, can witness synchronized squeezing between the cavity and mechanical modes. We investigate an OMS driven by two coherent fields, and demonstrate that optimal quantum correlations and squeezing synchronization can be [...] Read more.
In an optomechanical system (OMS), the dynamics of quantum correlations, e.g., quantum discord, can witness synchronized squeezing between the cavity and mechanical modes. We investigate an OMS driven by two coherent fields, and demonstrate that optimal quantum correlations and squeezing synchronization can be achieved by carefully tuning key parameters: the cavity-laser detunings, loss rates, and the effective coupling ratio between the optomechanical interaction and the amplitude drive. By employing the steady-state solution of the covariance matrix within the Lyapunov framework, we identify the conditions under which squeezing becomes stabilized. Furthermore, we demonstrate that synchronized squeezing of the cavity and mechanical modes can be effectively controlled by tuning the loss ratio between the cavity and mechanical subsystems. Alternatively, in the case where the cavity is driven by a single field, we demonstrate that synchronized squeezing in the conjugate quadratures of the cavity and mechanical modes can still be achieved, provided that the cavity is coupled to a squeezed reservoir. The presence of this engineered reservoir compensates the absent driving field, by injecting directional quantum noise, thereby enabling the emergence of steady-state squeezing correlations between the two modes. A critical aspect of our study reveals how the interplay between dissipative and driven-dispersive squeezing mechanisms governs the system’s bandwidth and robustness against decoherence. Our findings provide a versatile framework for manipulating quantum correlations and squeezing in OMS, with applications in quantum metrology, sensing, and the engineering of nonclassical states. This work advances the understanding of squeezing synchronization and offers new strategies for enhancing quantum-coherent phenomena in dissipative environments. Full article
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26 pages, 10233 KB  
Article
Time-Series Forecasting Method Based on Hierarchical Spatio-Temporal Attention Mechanism
by Zhiguo Xiao, Junli Liu, Xinyao Cao, Ke Wang, Dongni Li and Qian Liu
Sensors 2025, 25(13), 4001; https://doi.org/10.3390/s25134001 - 26 Jun 2025
Viewed by 892
Abstract
In the field of intelligent decision-making, time-series data collected by sensors serves as the core carrier for interaction between the physical and digital worlds. Accurate analysis is the cornerstone of decision-making in critical scenarios, such as industrial monitoring and intelligent transportation. However, the [...] Read more.
In the field of intelligent decision-making, time-series data collected by sensors serves as the core carrier for interaction between the physical and digital worlds. Accurate analysis is the cornerstone of decision-making in critical scenarios, such as industrial monitoring and intelligent transportation. However, the inherent spatio-temporal coupling characteristics and cross-period long-range dependency of sensor data cause traditional time-series prediction methods to face performance bottlenecks in feature decoupling and multi-scale modeling. This study innovatively proposes a Spatio-Temporal Attention-Enhanced Network (TSEBG). Breaking through traditional structural designs, the model employs a Squeeze-and-Excitation Network (SENet) to reconstruct the convolutional layers of the Temporal Convolutional Network (TCN), strengthening the feature expression of key time steps through dynamic channel weight allocation to address the redundancy issue of traditional causal convolutions in local pattern capture. A Bidirectional Gated Recurrent Unit (BiGRU) variant based on a global attention mechanism is designed, leveraging the collaboration between gating units and attention weights to mine cross-period long-distance dependencies and effectively alleviate the gradient disappearance problem of Recurrent Neural Network (RNN-like) models in multi-scale time-series analysis. A hierarchical feature fusion architecture is constructed to achieve multi-dimensional alignment of local spatial and global temporal features. Through residual connections and the dynamic adjustment of attention weights, hierarchical semantic representations are output. Experiments show that TSEBG outperforms current dominant models in time-series single-step prediction tasks in terms of accuracy and performance, with a cross-dataset R2 standard deviation of only 3.7%, demonstrating excellent generalization stability. It provides a novel theoretical framework for feature decoupling and multi-scale modeling of complex time-series data. Full article
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26 pages, 3424 KB  
Article
MFF: A Multimodal Feature Fusion Approach for Encrypted Traffic Classification
by Hong Huang, Yinghang Zhou, Feng Jiang, Xiaolin Zhou and Qingping Jiang
Electronics 2025, 14(13), 2584; https://doi.org/10.3390/electronics14132584 - 26 Jun 2025
Viewed by 675
Abstract
With the widespread adoption of encryption technologies, encrypted traffic classification has become essential for maintaining network security awareness and optimizing service quality. However, existing deep learning-based methods often rely on fixed-length truncation during preprocessing, which can lead to the loss of critical information [...] Read more.
With the widespread adoption of encryption technologies, encrypted traffic classification has become essential for maintaining network security awareness and optimizing service quality. However, existing deep learning-based methods often rely on fixed-length truncation during preprocessing, which can lead to the loss of critical information and degraded classification performance. To address this issue, we propose a Multi-Feature Fusion (MFF) model that learns robust representations of encrypted traffic through a dual-path feature extraction architecture. The temporal modeling branch incorporates a Squeeze-and-Excitation (SE) attention mechanism into ResNet18 to dynamically emphasize salient temporal patterns. Meanwhile, the global statistical feature branch uses an autoencoder for the nonlinear dimensionality reduction and semantic reconstruction of 52-dimensional statistical features, effectively preserving high-level semantic information of traffic interactions. MFF integrates both feature types to achieve feature enhancement and construct a more robust representation, thereby improving classification accuracy and generalization. In addition, SHAP-based interpretability analysis further validates the model’s decision-making process and reliability. Experimental results show that MFF achieves classification accuracies of 99.61% and 99.99% on the ISCX VPN-nonVPN and USTC-TFC datasets, respectively, outperforming mainstream baselines. Full article
(This article belongs to the Section Networks)
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15 pages, 2117 KB  
Article
Enhancement of Photon Blockade Under the Joint Effect of Optical Parametric Amplification and Mechanical Squeezing
by Yue Hao, Jia-Le Tong, Suying Bai, Shao-Xiong Wu and Cheng-Hua Bai
Photonics 2025, 12(7), 628; https://doi.org/10.3390/photonics12070628 - 20 Jun 2025
Viewed by 488
Abstract
The photon blockade effect, as a quantum behavior in cavity optomechanics, has certain limitations, including stringent requirements for system parameters and technical difficulties in achieving strong nonlinear interactions. This paper proposes a novel scheme that aims to achieve strong nonlinear effects through introducing [...] Read more.
The photon blockade effect, as a quantum behavior in cavity optomechanics, has certain limitations, including stringent requirements for system parameters and technical difficulties in achieving strong nonlinear interactions. This paper proposes a novel scheme that aims to achieve strong nonlinear effects through introducing the degenerate optical parametric amplifier (OPA) and mechanical squeezing. These enhanced nonlinear effects can significantly improve the photon blockade effect, effectively overcoming the limitations of weak coupling. Our theoretical analysis demonstrates the successful realization of an ideal single-photon blockade (1PB) state through optimized parameter conditions. Additionally, this joint approach significantly enhances the two-photon blockade (2PB) effect and broadens the region where 2PB occurs. This finding helps us identify the optimal system parameters to maximize two-photon emission efficiency. By precisely controlling these parameters, a new pathway is opened for more flexible manipulation and utilization of the photon blockade effect in experiments. Full article
(This article belongs to the Section Quantum Photonics and Technologies)
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28 pages, 4916 KB  
Article
Research on Bearing Fault Diagnosis Method for Varying Operating Conditions Based on Spatiotemporal Feature Fusion
by Jin Wang, Yan Wang, Junhui Yu, Qingping Li, Hailin Wang and Xinzhi Zhou
Sensors 2025, 25(12), 3789; https://doi.org/10.3390/s25123789 - 17 Jun 2025
Viewed by 868
Abstract
In real-world scenarios, the rotational speed of bearings is variable. Due to changes in operating conditions, the feature distribution of bearing vibration data becomes inconsistent, which leads to the inability to directly apply the training model built under one operating condition (source domain) [...] Read more.
In real-world scenarios, the rotational speed of bearings is variable. Due to changes in operating conditions, the feature distribution of bearing vibration data becomes inconsistent, which leads to the inability to directly apply the training model built under one operating condition (source domain) to another condition (target domain). Furthermore, the lack of sufficient labeled data in the target domain further complicates fault diagnosis under varying operating conditions. To address this issue, this paper proposes a spatiotemporal feature fusion domain-adaptive network (STFDAN) framework for bearing fault diagnosis under varying operating conditions. The framework constructs a feature extraction and domain adaptation network based on a parallel architecture, designed to capture the complex dynamic characteristics of vibration signals. First, the Fast Fourier Transform (FFT) and Variational Mode Decomposition (VMD) are used to extract the spectral and modal features of the signals, generating a joint representation with multi-level information. Then, a parallel processing mechanism of the Convolutional Neural Network (SECNN) based on the Squeeze-and-Excitation module and the Bidirectional Long Short-Term Memory network (BiLSTM) is employed to dynamically adjust weights, capturing high-dimensional spatiotemporal features. The cross-attention mechanism enables the interaction and fusion of spatial and temporal features, significantly enhancing the complementarity and coupling of the feature representations. Finally, a Multi-Kernel Maximum Mean Discrepancy (MKMMD) is introduced to align the feature distributions between the source and target domains, enabling efficient fault diagnosis under varying bearing conditions. The proposed STFDAN framework is evaluated using bearing datasets from Case Western Reserve University (CWRU), Jiangnan University (JNU), and Southeast University (SEU). Experimental results demonstrate that STFDAN achieves high diagnostic accuracy across different load conditions and effectively solves the bearing fault diagnosis problem under varying operating conditions. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
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30 pages, 5714 KB  
Article
Analysis of Unbalance Response and Vibration Reduction of an Aeroengine Gas Generator Rotor System
by Haibiao Zhang, Xing Heng, Ailun Wang, Tao Liu, Qingshan Wang and Kun Liu
Lubricants 2025, 13(6), 266; https://doi.org/10.3390/lubricants13060266 - 15 Jun 2025
Viewed by 929
Abstract
To ensure the vibration safety of rotor support systems in modern aeroengines, this study develops a dynamic model of the aeroengine gas generator rotor system and analyzes its complex unbalance response characteristics. Subsequently, it investigates vibration reduction strategies based on these response patterns. [...] Read more.
To ensure the vibration safety of rotor support systems in modern aeroengines, this study develops a dynamic model of the aeroengine gas generator rotor system and analyzes its complex unbalance response characteristics. Subsequently, it investigates vibration reduction strategies based on these response patterns. This study begins by developing individual dynamic models for the disk–blade system, the circular arc end-teeth connection structure and the squeeze film damper (SFD) support system. These models are then integrated using the differential quadrature finite element method (DQFEM) to create a comprehensive dynamic model of the gas generator rotor system. The unbalance response characteristics of the rotor system are calculated and analyzed, revealing the impact of the unbalance mass distribution and the combined support system characteristics on the unbalance response of the rotor system. Drawing on the obtained unbalance response patterns, the vibration reduction procedures for the rotor support system are explored and experimentally verified. The results demonstrate that the vibration response of the modern aeroengine rotor support system can be reduced by adjusting the unbalance mass distribution, decreasing the bearing stiffness and increasing the bearing damping, thereby enhancing the vibration safety of the rotor system. This study introduces a novel integration of DQFEM with detailed component-level modeling of circular arc end-teeth connections, disk–blade interactions and SFD dynamics. This approach uniquely captures the coupled effects of unbalance distribution and support system characteristics, offering a robust framework for enhancing vibration safety in aeroengine rotor systems. The methodology provides both theoretical insights and practical guidelines for optimizing rotor dynamic performance under unbalance-induced excitations. Full article
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11 pages, 1779 KB  
Article
Long-Range Interactions Between Neighboring Nanoparticles Tuned by Confining Membranes
by Xuejuan Liu, Falin Tian, Tongtao Yue, Kai Yang and Xianren Zhang
Nanomaterials 2025, 15(12), 912; https://doi.org/10.3390/nano15120912 - 12 Jun 2025
Viewed by 471
Abstract
Membrane tubes, a class of soft biological confinement for ubiquitous transport intermediates, are essential for cell trafficking and intercellular communication. However, the confinement interaction and directional migration of diffusive nanoparticles (NPs) are widely dismissed as improbable due to the surrounding environment compressive force. [...] Read more.
Membrane tubes, a class of soft biological confinement for ubiquitous transport intermediates, are essential for cell trafficking and intercellular communication. However, the confinement interaction and directional migration of diffusive nanoparticles (NPs) are widely dismissed as improbable due to the surrounding environment compressive force. Here, combined with the mechanics analysis of nanoparticles (such as extracellular vesicles, EVs) to study their interaction in confinement, we perform dissipative particle dynamics (DPD) simulations to construct a model that is as large as possible to clarify the submissive behavior of NPs. Both molecular simulations and mechanical analysis revealed that the interactions between NPs are controlled by confinement deformation and the centroid distance of the NPs. When the centroid distance exceeds a threshold value, the degree of crowding variation becomes invalid for NPs motion. The above conclusions are further supported by the observed dynamics of multiple NPs under confinement. These findings provide new insights into the physical mechanism, revealing that the confinement squeeze generated by asymmetric deformation serves as the key factor governing the directional movement of the NPs. Therefore, the constraints acting on NPs differ between rigid confinement and soft confinement environments, with NPs maintaining relative stillness in rigid confinement. Full article
(This article belongs to the Section Synthesis, Interfaces and Nanostructures)
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11 pages, 1024 KB  
Article
Parametric Interaction-Induced Asymmetric Behaviors in a Coupled-Cavities Quantum Electrodynamics System
by Xu Ma, Dexi Guo, Chengjie Zhu and Jingping Xu
Photonics 2025, 12(6), 563; https://doi.org/10.3390/photonics12060563 - 4 Jun 2025
Viewed by 569
Abstract
We investigate a quantum electrodynamics system consisting of two coupled single-mode cavities. The left cavity couples with a two-level atom, while the right cavity incorporates a second-order nonlinear medium, activated by a pumping field. In the absence of nonlinear medium, we show that [...] Read more.
We investigate a quantum electrodynamics system consisting of two coupled single-mode cavities. The left cavity couples with a two-level atom, while the right cavity incorporates a second-order nonlinear medium, activated by a pumping field. In the absence of nonlinear medium, we show that the transmitted field intensity reveals only classical asymmetric behavior at the central frequency. However, the parametric interaction induced by the nonlinear medium leads to various quantum asymmetric behaviors at single photon excitation frequencies, including the squeezing, quantum statistics, and phase-space characteristics of the transmitted photons. These asymmetric behaviors arise from additional excitation pathways enabled by the parametric interaction-induced two-photon processes. We demonstrate these asymmetric behaviors through Klyshko’s figures of merit, the Wigner function, and the steady-state second-order correlation function of the transmitted photons. These results present promising applications for remote quantum-state manipulation and contribute significantly to the advancement of quantum networking. Full article
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