Search Results (251)

Fire trucks equipped with truck-mounted electric water cannons are key mobile firefighting assets for urban and industrial fire response. However, due to the inherent mechanical inertia of the cannon body, its low-frequency motion response cannot match high-frequency control commands, making the system prone to oscillations and control instability. To address this command–execution frequency mismatch, this paper proposes a decoupled dual closed-loop control architecture for truck-mounted electric water cannons on mobile fire trucks: the fast loop is used for fire-source tracking and rapid localization, while the slow loop is used for water-jet aiming alignment. In the fast loop, a 2-D quadrant positioning rule drives the pan–tilt unit to achieve rapid fire tracking and accurate centering. In the slow loop, Kalman-filter-based state estimation and delay-aligned prediction generate feedforward aiming commands; these commands are fused with error feedback and further processed through command limiting and trajectory optimization, ultimately producing smooth and executable angle references. The visual perception module ran at 58 FPS, satisfying the real-time requirement of the proposed system. In five repeated extinguishment tests under controlled open-site conditions, the proposed method successfully completed all trials and reduced the mean extinguishment time to 13.55 s, compared with 15.83 s for the incremental-PID baseline and 23.76 s for the coupled proportional baseline, while also showing smoother correction and less redundant oscillation. Full article

To solve the problems of high-precision attitude control and vibration isolation of satellite payloads, this paper conducts in-depth research on satellite attitude dynamics and maglev active vibration isolation control technology. A dual-super collaborative control scheme is proposed, which consists of payload module ultra-high precision and ultra-high stability control, relative position control of two modules, and service module attitude control. The target attitude and angular velocity obtained by maneuver path planning and attitude guidance are transmitted to the attitude and orbit control management unit, and the total control command torque is formed by combining feedback control and feedforward control, which is then distributed to each maglev actuator to realize high-precision control of the payload module. The architecture of the maglev vibration isolation system is designed, and its dynamic model is established based on the Newton–Euler equation. Meanwhile, the dynamic model of the maglev actuator is constructed, and the active control strategy is designed by adopting PID control. The models of output force and torque are established, system parameters are set for simulation analysis of dynamic responses such as displacement, attitude and electromagnetic force, and a 20% pull-bias robustness test is carried out. Simulation results show that the system has high isolation accuracy, stability, and can effectively suppress the interference and shaking of the platform and load, with strong robustness. Full article

In nanoscale wafer defect inspection, raster scan imaging imposes sub-micrometer requirements on motion stage tracking accuracy, while trajectory changes and load variations pose significant challenges to traditional control methods. This paper proposes a Real-time Iterative Compensation based Adaptive Robust Control (RICARC) strategy. Within this framework, the ARC module incorporates RLS-based online parameter estimation, a PID-type feedback control term, and a robust control term to suppress lumped disturbances. On this basis, the RIC module establishes a discrete prediction model based on the ARC closed-loop system and iteratively generates optimal feedforward compensation signals at each sampling instant to further suppress residual tracking errors. Experimental results across five operating scenarios, including periodic, dual-frequency, and S-curve trajectories, as well as payload variation, and strong external disturbances, demonstrate that RICARC consistently achieves sub-micrometer RMS accuracy ranging from 0.120 to 0.240 μm, reducing RMS errors by over 75% compared with conventional ARC, effectively enhancing imaging quality in nanoscale wafer defect detection systems. Full article

Physical Unclonable Functions (PUFs) represent a highly promising hardware security primitive, yet they face constraints of insufficient reliability and threats from modeling attacks. This paper designs a novel Feed-Forward Crossbar Matrix Arbiter PUF (FC-MA PUF). It incorporates an inter-stage crossbar structure, a feed-forward control system, and a mechanism for selecting reliable challenge-response pairs. These features significantly enhance the structural non-linearity and stability, substantially improving security and adaptability to a wider range of operating environments. It provides a high-strength authentication solution with low resource overhead for lightweight security-demanding devices such as IoT devices. The proposed FC-MA PUF has been successfully implemented on a Field-Programmable Gate Array (FPGA) platform. Experimental results for the selected 4-stage FC-MA PUF configuration show a bias, inter-chip uniqueness, and bit error rate (BER) of 49.88%, 49.68%, and 0.018%, respectively. Furthermore, the structure allows for flexible configuration of the number of feed-forward modules based on practical application requirements: a greater number of feed-forward modules enhances security but also leads to an increased BER and a decreased proportion of stable challenge-response pairs. Experimental results based on a training set of 1,000,000 challenge-response pairs demonstrate that: with two feed-forward units, the stable (Challenge Response Pair)CRP ratio is 39.72% and the Covariance Matrix Adaptation Evolutionary Strategies (CMA-ES) attack prediction success rate is 58.20%; with three units, the ratio decreases to 29.12% and the prediction rate drops to 54.91%; with four units, these values further decline to 20.18% and 52.33% respectively. These results confirm that the proposed FC-MA PUF effectively resists multiple modeling attacks, including Logistic Regression (LR), Support Vector Machine (SVM), and CMA-ES. Full article

Precise 3D reconstruction of non-cooperative space targets is a prerequisite for active debris removal and on-orbit servicing. However, this task is impeded by severe environmental challenges. Specifically, the limited dynamic range of visible light cameras leads to frequent overexposure or underexposure under extreme space lighting. Compounded by sparse textures and strong specular reflections, these factors significantly constrain reconstruction accuracy. While existing general-purpose feed-forward models such as MapAnything offer efficient inference, their geometric recovery capabilities degrade sharply when facing significant domain shifts. To address these issues, this paper proposes an enhanced 3D reconstruction framework tailored for the space environment named In-Orbit MapAnything. First, to mitigate data scarcity, we construct a high-quality space target dataset incorporating extreme illumination characteristics, which provides comprehensive auxiliary modalities including accurate camera poses and dense point clouds. Second, we propose the SatMap-Adapter module to mitigate feature degradation caused by severe specular reflections. This architecture employs a hierarchical cascade sampling strategy to align multi-level backbone features and utilizes a lightweight adaptive fusion module to dynamically integrate shallow photometric cues, intermediate structural information, and deep semantic features. Finally, we employ a weight-decomposed low-rank adaptation strategy to achieve parameter-efficient fine-tuning while strictly freezing the pre-trained backbone. Experimental results demonstrate that the proposed method decreases the absolute relative error and Chamfer distance by 15.23% and 20.02% respectively compared to the baseline MapAnything model, while maintaining a rapid inference speed. The proposed approach effectively suppresses reconstruction noise on metallic surfaces and recovers fine geometric structures, validating the effectiveness of our feature-enhanced framework in extreme space environments. Full article

Gallbladder diseases spanning cholelithiasis, cholecystitis, and gallbladder cancer represent a clinically heterogeneous continuum in which type 2 diabetes mellitus (T2DM) acts as a key metabolic modifier. Conventional models centered on bile supersaturation alone do not sufficiently account for the persistent inflammation and inter-individual variability frequently observed in practice. Here, we synthesize emerging evidence implicating the gut microbiota–bile acid (BA) axis as an integrative mechanism linking metabolic dysregulation, barrier dysfunction, and biliary pathobiology in the diabetic host. Hyperglycemia and insulin resistance, together with impaired mucosal resilience, are associated with shifts in microbial community structure and BA-transforming functions (e.g., bile salt hydrolase and 7α-dehydroxylation), favoring a more hydrophobic BA pool. These changes may disrupt BA receptor signaling, including FXR–FGF15/19 and TGR5-related pathways, thereby amplifying metabolic inflammation, promoting lithogenic bile formation, and impairing gallbladder motility. In parallel, barrier vulnerability may facilitate microbial translocation and LPS-driven immune activation, reinforcing a feed-forward loop that supports the gallstone–inflammation–carcinogenesis trajectory. Translationally, microbiome- and BA-oriented strategies (dietary patterns, bile acid therapeutics, and targeted microbiome modulation) are promising adjuncts, yet precision management should explicitly consider medication- and weight loss–related confounding—particularly with incretin-based therapies—to optimize biliary outcomes across disease stages. Full article

As a critical actuator in aero-engine control systems, the health condition of the Fuel Metering Unit (FMU) directly influences flight safety and maintenance efficiency, making the precise prediction of its degradation process a core task in the engine’s Prognostic and Health Management (PHM). This paper presents a novel inverted stabilized LSTM-Transformer (isLTransformer) approach for predicting the health state of aero-engine FMUs, addressing the limitations of existing methods in modeling long-sequence multivariate data. Firstly, a Composite Health Indicator (CHI) is constructed through semi-supervised learning (SSL), which fuses multi-sensor monitoring data to quantitatively characterize the degradation trend of the FMU throughout its operational lifecycle. Secondly, the proposed isLTransformer model is designed by replacing the feedforward network in traditional iTransformer with a stabilized LSTM module, which maintains the self-attention mechanism’s capability to explicitly model dynamic correlations between multiple variables while enhancing the ability to capture nonlinear degradation within individual variables. A physical FMU test bench is designed for the real-world PHM degradation experiments, and the collected dataset was used to demonstrate the effectiveness of the proposed method. Evaluation metrics, including Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), are employed to assess the prediction accuracy. The proposed method demonstrates high monotonicity and trend consistency in CHI construction. Compared to the inverted Transformer (iTransformer) and iTransformer- Bi-directional Long Short-Term Memory (BiLSTM), the proposed isLTransformer framework demonstrates significantly reduced prediction errors, validating its superiority in multivariate long-sequence prediction tasks and effectiveness for aero-engine FMU health prediction. Full article

Artificial intelligence computing systems increasingly demand high-bandwidth, high-extinction-ratio, chip-to-chip optical transceivers. Silicon microring modulators (MRMs) are attractive for such transmitters due to their compact footprint and wavelength-division multiplexing capability. However, for a specified extinction ratio, the optical bandwidth for high-Q MRMs and the driver’s RC time constant prevent conventional single-segment MRM drivers from supporting 100 GBaud class PAM4 transmission. This work presents a broadband driver exploiting the feedforward technique for dual-segment MRMs. It extends electro-optical bandwidth while maintaining a high Q-factor and extinction ratio. The input signal is split into low- and high-frequency components that drive the long and short segments of the MRM, respectively. The long segment uses a broadband low-pass driver, whereas the short segment employs a driver with a programmable bandpass response near the Nyquist frequency. The design space is obtained from an equivalent electro-optical model under constant group-delay constraints. Simulations at 1310 nm show that the 3 dB electro-optical bandwidth improves from ~50 to >70 GHz and that a 200 Gb/s PAM4 optical eye diagram exhibits an open eye; the energy efficiency is 1.44 pJ/bit, and the extinction ratio improves from 2 dB to 4.1 dB. The proposed technique provides a tunable electro-optical co-design approach for high-bandwidth-density, high-extinction-ratio silicon photonic transmitters. Full article

Neurodegenerative diseases share a mitochondrial–immune axis in which impaired oxidative phosphorylation reshapes neuronal metabolism and drives chronic inflammation. Complex I play a redox gatekeeper role at the coenzyme Q (CoQ) junction: catalytic defects, misassembly, or reverse electron transport over-reduce the CoQ pool, increase electron leak, and elevate ROS. How respiratory supercomplex plasticity (CI-CIII₂, CIII₂-CIV_n, or CI-CIII₂-CIV_n) modulates carrier channelling, flux control, and ROS propensity through dynamic reorganization of the electron transport chain is highlighted. Excess ROS damages lipids and mitochondrial DNA, promoting the release of mitochondrial damage-associated molecular patterns s that activate NLRP3 inflammasome signalling, cGAS-STING-dependent interferon programs, and endosomal TLR9 pathways, establishing feed-forward loops between mitochondrial injury and neuroinflammation. Disease-focused sections integrate evidence from Parkinson’s, Alzheimer’s, amyotrophic lateral sclerosis, and Huntington’s models, and map these mechanisms onto therapeutic opportunities spanning electron transport chain support, supercomplex stabilization, and consider mtDNA-sensing inflammatory nodes. Full article

Underwater imaging is generally subject to complex degradation issues such as color distortion, contrast degradation, and detail blurring due to the selective absorption and scattering of light wavelengths by water. Existing deep learning methods have limitations in the collaborative optimization of local details and global color. To address this issue, this paper proposes a multi-scale enhancement network based on global and local feature fusion. By integrating the advantages of CNN and Transformer, it achieves joint optimization of global color correction and local detail enhancement. Specifically, MGLFNet extracts global and local features of the image through the global and local feature fusion block in the core component of the multi-scale convolution–Transformer block and performs dynamic fusion. Meanwhile, to extract features at different scales to enhance performance, we design a multi-scale convolution feed-forward network. Through the action of the fusion module and the feed-forward network, a color-rich and detail-clear enhanced image is obtained. A large number of experimental results show that MGLF-Net outperforms comparison methods in both qualitative and quantitative evaluations of visual quality, with PSNR and SSIM values of 25.37 and 0.918 on the UIEB dataset, respectively, as well as low memory usage and computational resource requirements. In addition, detailed ablation experiments prove the effectiveness of the core components of the model. Full article

To address the issues of insufficient restoration of texture details in deblurred images and inadequate learning of frequency domain features, an image deblurring algorithm based on frequency domain feature enhancement and convolutional neural networks is proposed. In this architecture, firstly, a Fourier residual module with a parallel structure is constructed to achieve collaborative learning and modeling of spatial and frequency domain features, aiming to improve frequency domain feature learning capability and the restoration effect of the texture details; secondly, a gated controlled feed-forward unit acts on the Fourier residual module to further enhance the nonlinear expression ability of the algorithm; thirdly, a supervised attention module is improved and added to the decoder to promote more effective capture of key features for image reconstruction; finally, the weighted sum of spatial domain Charbonnier loss function and frequency domain loss function is defined as a novel total loss function. In addition, to verify the performance of our proposed algorithm, we conducted experiments on the GOPRO and HIDE datasets. Through experiments on the GOPRO, we obtained an SSIM and an LPIPS of 0.961 and 0.0278, respectively. With regard to the experiments on the HIDE datasets, we obtained an SSIM and an LPIPS of 0.941 and 0.0286, respectively. As for parameter count and running time, their values were 1.197 and 9.15 × 10⁶, respectively, obtained by the experiments on the GOPRO. In all algorithms, the values of our proposed algorithm are optimal. However, the PSNR of our proposed algorithm is very close to that of the latest comparison algorithm and is suboptimal. In a word, experimental results have demonstrated that our proposed algorithm effectively removes blur while better preserving the details and edges of the image. Therefore, it has more practical value and prospects in computer vision tasks. Full article

Remote sensing open-vocabulary object detection focuses on identifying and localizing unseen categories within remote sensing imagery. However, constrained by characteristics such as dense target distribution, complex background interference, and drastic scale variations inherent to remote sensing scenarios, existing methods are prone to background noise interference when extracting features from dense, small target regions. This leads to weakened semantic representation and reduced localization accuracy. Therefore, we propose RS-DINO to address these challenges. Specifically: Firstly, to address the issue of small features being obscured by the background, the feature extraction module incorporates a multi-scale large-kernel attention mechanism. This expands the receptive field while enhancing local detail modelling, significantly improving the feature representation of minute targets. Secondly, a cross-modal feature fusion module employing bidirectional cross-attention achieves deep alignment between image and textual features. Subsequently, a language-guided query selection mechanism enhances detection accuracy through hybrid query strategies. Finally, to enhance the spatial sensitivity and channel adaptability of fusion features, the multimodal decoder integrates a convolutional gated feedforward network, significantly boosting the model’s robustness in dense, multi-scale scenes. Experiments on DIOR, DOTA v2.0, and NWPU-VHR10 demonstrate substantial gains, with fine-tuned RS-DINO surpassing existing methods by 3.5%, 3.7%, and 4.0% in accuracy, respectively. Full article

This paper develops an extended-state-observer (ESO)-enhanced actor–critic reinforcement learning (RL) scheme for the trajectory tracking control of 3-DOF marine vessels subject to uncertain hydrodynamics and environmental disturbances. A coordinate-consistent error construction is provided to obtain an exact strict-feedback second-order uncertain template. On this basis, an Hamilton–Jacobi–Bellman (HJB)-inspired optimised control structure is implemented: the critic approximates the optimal value-gradient and the actor generates the optimised control law. A key simplification is employed: rather than minimising the squared Bellman residual via complex gradients, we introduce an HJB-inspired actor–critic consistency regularisation through a weight-matching coupling. This yields computationally light online update laws and enables transparent Lyapunov-based stability analysis while not claiming exact HJB satisfaction or policy optimality. The ESO estimates lumped uncertainty and provides feedforward compensation, so the RL module learns only the observer residual. A composite Lyapunov analysis establishes the semi-global uniform ultimate boundedness of tracking errors and boundedness of all observer signals. Practical implementation with thruster allocation, explicit wind–wave–current disturbance shaping filters, and a theory-aligned ablation protocol are provided for reproducibility. Full article

In recent years, Transformer architectures have excelled at modeling non-local information. This makes them suitable for image deraining. However, existing methods use dense self-attention. They compute all similarities between query and key tokens. This is inefficient. In practice, this approach can lead to the neglect of the most relevant information and result in a blurring effect of irrelevant representations during the feature aggregation process. To address this issue, this paper proposes an image deraining Transformer based on sparse non-local self-attention. The core of the network consists of multiple non-local feature extraction modules, primarily comprising a sparse self-attention network and a sparse feedforward network along the channel dimension. Specifically, we implement sparse attention by selecting the most useful similarities based on Top-k approximations. Furthermore, we have developed a sparse feedforward network to achieve more accurate representations for high-quality preservation results. Extensive experiments on benchmark datasets have demonstrated the effectiveness of our proposed method. Full article

Extracellular matrix (ECM) stiffening is a defining biophysical feature of solid tumors that reshape gene regulation through mechanotransduction. Increased collagen crosslinking and stromal remodeling enhance integrin engagement, focal-adhesion signaling and force transmission to the nucleus, where key hubs such as lysyl oxidase (LOX), focal adhesion kinase (FAK) and the Hippo co-activators YAP1 and TAZ (WWTR1) promote proliferation, invasion, stemness and therapy resistance. Here, we synthesize evidence that quantitative changes in matrix stiffness remodel the miRNome and lncRNome in both tumor and stromal compartments, including extracellular vesicle cargo that reprograms metastatic niches. To address heterogeneity in experimental support, we classify mechanosensitive ncRNAs into studies directly validated by stiffness manipulation (e.g., tunable hydrogels/AFM) versus indirect associations based on mechanosensitive signaling, and we summarize physiological versus pathophysiological stiffness ranges across tissues discussed. We further review competing endogenous RNA (ceRNA) networks converging on mechanotransduction nodes and ECM remodeling enzymes, and discuss translational opportunities and challenges, including targeting mechanosensitive ncRNAs, combining ncRNA modulation with anti-stiffening strategies, delivery barriers in dense tumors, and the potential of circulating/exosomal ncRNAs as biomarkers. Overall, integrating ECM mechanics with ncRNA regulatory circuits provides a framework to identify feed-forward loops sustaining aggressive phenotypes in rigid microenvironments and highlights priorities for validation in physiologically relevant models. Full article