Search Results (158)

Schlieren imaging is a powerful optical sensing technique that captures flow-induced refractive index gradients, offering valuable visual data for analyzing complex fluid dynamics. However, the large volume and structural complexity of the data generated by this sensor pose significant challenges for extracting key physical insights and performing efficient reconstruction and temporal prediction. In this study, we propose a Spatial-Frequency-Scale variational autoencoder (SFS-VAE), a deep learning framework designed for the unsupervised feature decomposition of Schlieren sensor data. To address the limitations of traditional $\beta$-variational autoencoder ($\beta$-VAE) in capturing complex flow regions, the Progressive Frequency-enhanced Spatial Multi-scale Module (PFSM) is designed, which enhances the structures of different frequency bands through Fourier transform and multi-scale convolution; the Feature-Spatial Enhancement Module (FSEM) employs a gradient-driven spatial attention mechanism to extract key regional features. Experiments on flat plate film-cooled jet schlieren data show that SFS-VAE can effectively preserve the information of the mainstream region and more accurately capture the high-gradient features of the jet region, reducing the Root Mean Square Error (RMSE) by approximately 16.9% and increasing the Peak Signal-to-Noise Ratio (PSNR) by approximately 1.6 dB. Furthermore, when integrated with a Transformer for temporal prediction, the model exhibits significantly improved stability and accuracy in forecasting flow field evolution. Overall, the model’s physical interpretability and generalization ability make it a powerful new tool for advanced flow diagnostics through the robust analysis of Schlieren sensor data. Full article

The rapid evolution of modern power systems, driven by the large-scale integration of renewable energy sources and the emergence of smart grids, presents new challenges in maintaining grid stability, power quality, and control reliability. As critical interfacing elements, three-phase pulse width modulation (PWM) converters must now ensure resilient and efficient operation under increasingly adverse and dynamic grid conditions. This paper proposes an adaptive neural network-based virtual flux (VF) estimator for sensorless predictive direct power control (PDPC) of PWM converters under nonideal grid voltage conditions. The proposed estimator is realized using an adaptive linear neuron (ADALINE) configured as a quadrature signal generator, offering robustness against grid voltage disturbances such as voltage unbalance, DC offset and harmonic distortion. In parallel, a PDPC scheme based on the extended pq theory is developed to reject active-power oscillations and to maintain near-sinusoidal grid currents under unbalanced conditions. The resulting VF-based PDPC (VF-PDPC) strategy is validated via real-time simulations on the OPAL-RT platform. Comparative analysis confirms that the ADALINE-based estimator surpasses conventional VF estimation techniques. Moreover, the VF-PDPC achieves superior performance over conventional PDPC and extended pq theory-based PDPC strategies, both of which rely on physical voltage sensors, confirming its robustness and effectiveness under non-ideal grid conditions. Full article

Low-frequency oscillation modes are studied in small-signal angular stability because, if not adequately damped, they can cause power system instability in the event of a contingency. The interconnection and expansion of large power systems has led to the emergence of multiple local and inter-area modes and required new damping control strategies for these modes. The expansion of the use of Phasor Measurement Units in power systems has led to the development of new control strategies such as Wide-Area Damping Controllers (WADCs) that use data from PMUs to dampen low-frequency oscillations. Although the benefits of WADCs are promising, there are challenges in designing a WADC. This paper proposes a two-stage algorithm for the robust design of a WADC for modern power systems. The first stage consists of solving an optimization model and finding the WADC parameters that maximize the damping ratios of all modes of the linearized system model for a set of operating points. The second stage consists of refining the WADC parameters through an iterative algorithm. Cases were studied for a set of IEEE 68-bus operating points through modal analysis and time-domain simulations. The results obtained demonstrated the good performance of the proposed two-stage algorithm compared with an existing WADC design method based on a Linear Quadratic Regulator. Full article

In this work, a controller hardware-in-the-loop (CHIL) simulation of a grid-connected three-phase inverter equipped with an LCL filter is implemented using a real-time digital simulator (RTDS) as the plant and a digital signal processor (DSP) as the control hardware. This work identifies and discusses the critical aspects of the CHIL implementation process, emphasizing the relevance of the control delays that arise from sampling, computation, and pulse width modulation (PWM), which also adversely affect system stability, accuracy, and performance. Time and frequency domains are used to validate the modeling of the system, either to represent large-signal or small-signal models. This work shows multiple representations of the system under study: the fundamental frequency model, the switched model, and the switched model controlled by the DSP, are used to validate the nonlinear model, whereas the impedance-based modeling is followed to validate the linear representation. The results demonstrate a strong correlation among the models, confirming that the delay effects are accurately captured in the different simulation approaches. This comparison provides valuable insights into configuration practices that improve the fidelity of CHIL-based validation and supports impedance-based stability analysis in power electronic systems. The findings are particularly relevant for wideband modeling and real-time studies in electromagnetic transient analysis. Full article

In order to enhance the performance of 112 Gb/s polarization-multiplexed quadrature phase-shift keying (PM-QPSK) coherent optical receivers, a novel digital signal processing (DSP) framework is presented in this study. The suggested method combines cutting-edge signal processing techniques to address important constraints in long-distance, high data rate coherent systems. The framework uses overlap frequency domain equalization (OFDE) for chromatic dispersion (CD) compensation, which offers a cheaper computational cost and higher dispersion control precision than traditional time-domain equalization. An adaptive carrier phase recovery (CPR) technique based on mean-squared differential phase (MSDP) estimation is incorporated to manage phase noise induced by cross-phase modulation (XPM), providing dependable correction under a variety of operating situations. When combined, these techniques significantly increase Q factor performance, and optimum systems can handle transmission distances of up to 2400 km. The suggested DSP approach improves phase stability and dispersion tolerance even in the presence of nonlinear impairments, making it a viable and effective choice for contemporary coherent optical networks. The framework’s competitiveness was evaluated by comparing it against the most recent, cutting-edge DSP methods that were released after 2021. These included CPR systems that were based on kernels, transformers, and machine learning. The findings show that although AI-driven approaches had the highest absolute Q factors, they also required a large amount of computing power. On the other hand, the suggested OFDE in conjunction with adaptive CPR achieved Q factors of up to 11.7 dB over extended distances with a significantly reduced DSP effort, striking a good balance between performance and complexity. Its appropriateness for scalable, long-haul 112 Gb/s PM-QPSK systems is confirmed by a complexity versus performance trade-off analysis, providing a workable and efficient substitute for more resource-intensive alternatives. Full article

This study develops a new integral sliding mode-based method to address the decentralized adaptive fault-tolerant guaranteed cost control (GCC) problem via a dynamic event-triggered (DET) adaptive dynamic programming (ADP) approach. Firstly, integral sliding mode control technology is applied to eliminate the influence of actuator faults, which can guarantee that the large-scale system states stay on the sliding mode surface. Secondly, the ADP algorithm based on DET mode is employed to improve the control performance for equivalent sliding mode surface and reduce computational and communication overhead. Meanwhile, the GCC method is introduced to ensure that the performance cost function is less than an upper bound while maintaining system stability. Then, through Lyapunov stability analysis, it is proven that the presented DET-GCC method based on ADP algorithm can guarantee that all signals are uniformly ultimately bounded. Finally, the validity of the developed approach is confirmed through the simulation results. Full article

In the field of structural engineering, accurate identification of modal damping ratio is the key to structural dynamic response analysis. In order to accurately identify the modal damping ratio of the structure, this study proposes a method to identify the first-order modal damping ratio of the structure by analyzing the free attenuation response of the acceleration signal. By intercepting the free attenuation section from the structural dynamic response output, the amplitude is extracted, and the logarithmic estimation slope of the amplitude is fitted by the least square method to establish a theoretical model for identifying the first-order modal damping ratio. The results show that the method has high accuracy and good stability when the modal damping ratio is in the range of 0.00500~0.06400, and different nodes have little effect on the accuracy of identification. When the modal damping ratio is in the range of 0.06400~0.07000, the accuracy of the method is relatively low and the stability is relatively poor, but it is still within the acceptable range. When the damping ratio is greater than 0.07000 or less than 0.00500, the accuracy may be reduced. In order to further verify the effectiveness of the method, it is applied to the damping identification of a steel arch bridge project. The dynamic response of the bridge under random excitation and El Centro seismic wave excitation is analyzed by using the recommended value and identification value of the first-order damping ratio. The results show that the method can accurately and reliably identify the first-order modal damping ratio, which is significantly different from the empirical modal damping ratio. The identified modal damping ratio can more accurately describe the dynamic response of the structure after long-term use, while the recommended value is not applicable. This method can be applied to the modal damping ratio identification of other structural types, which reflects that the modal damping ratio identification method proposed in this study has certain engineering significance. It is worth noting that the accuracy of identification will be reduced when the modal damping ratio is less than 0.00500 or more than 0.07000, and it may not even be applicable if the modal damping ratio is too small or too large. This method has higher requirements for acceleration signals. In engineering, it may be affected by noise and other factors, resulting in reduced identification accuracy. In practical engineering, it is necessary to improve the identification accuracy of first-order modal damping ratio by changing the interception point of the free attenuation section of the acceleration signal and the screening of the amplitude. Full article

Background/Objectives: Constitutive activation of the PI3K/AKT/mTOR signaling cascade underlies the aggressive phenotype of fibroblast-like synoviocytes (FLSs) in rheumatoid arthritis (RA); however, a quantitative synthesis of in vitro data on pathway inhibition remains lacking. This systematic review and meta-analysis aimed to (i) aggregate standardized effects of pathway inhibitors on proliferation, apoptosis, migration/invasion, IL-6/IL-8 secretion, p-AKT, and LC3; (ii) assess heterogeneity and identify key moderators of variability, including stimulus type, cell source, and inhibitor class. Methods: PubMed, Europe PMC, and the Cochrane Library were searched up to 18 May 2025 (PROSPERO CRD420251058185). Twenty of 2684 screened records met eligibility. Two reviewers independently extracted data and assessed study quality with SciRAP. Standardized mean differences (Hedges g) were pooled using a Sidik–Jonkman random-effects model with Hartung–Knapp confidence intervals. Heterogeneity (τ², I²), 95% prediction intervals, and meta-regression by cell type were calculated; robustness was tested with REML-HK, leave-one-out, and Baujat diagnostics. Results: PI3K/AKT/mTOR inhibition markedly reduced proliferation (to –5.1 SD), IL-6 (–11.1 SD), and IL-8 (–6.5 SD) while increasing apoptosis (+2.7 SD). Fourteen of seventeen outcome clusters showed large effects (|g| ≥ 0.8), with low–moderate heterogeneity (I² ≤ 35% in 11 clusters). Prediction intervals crossed zero only in small k-groups; sensitivity analyses shifted pooled estimates by ≤0.05 SD. p-AKT and p-mTOR consistently reflected functional changes and emerged as reliable pharmacodynamic markers. Conclusions: Targeted blockade of PI3K/AKT/mTOR robustly suppresses the proliferative and inflammatory phenotype of RA-FLSs, reaffirming this axis as a therapeutic target. The stability of estimates across multiple analytic scenarios enhances confidence in these findings and highlights p-AKT and p-mTOR as translational response markers. The present synthesis provides a quantitative basis for personalized dual-PI3K/mTOR strategies and supports the adoption of standardized long-term preclinical protocols. Full article

The rapid densification of wireless networks demands efficient evaluation of special functions underpinning system-level performance metrics. To facilitate research, we introduce a computational framework tailored for the zero-balanced Gauss hypergeometric function $\Psi (x,y)\triangleq {}_{2}F_1(1,x;1+x;-y)$, a fundamental mathematical kernel emerging in Signal-to-Interference-plus-Noise Ratio (SINR) coverage analysis of non-uniform cellular deployments. Specifically, we propose a novel Reciprocal-Argument Transformation Algorithm (RTA), derived rigorously from a Mellin–Barnes reciprocal-argument identity, achieving geometric convergence with $O\left(1/y\right)$. By integrating RTA with a Pfaff-series solver into a hybrid algorithm guided by a golden-ratio switching criterion, our approach ensures optimal efficiency and numerical stability. Comprehensive validation demonstrates that the hybrid algorithm reliably attains machine-precision accuracy ($\sim {10}^{-16}$) within 1 μs per evaluation, dramatically accelerating calculations in realistic scenarios from hours to fractions of a second. Consequently, our method significantly enhances the feasibility of tractable optimization in ultra-dense non-uniform cellular networks, bridging the computational gap in large-scale wireless performance modeling. Full article

Artemisia absinthium L. contributes to ecological stabilization in arid regions through its deep root system for sand fixation and soil microenvironment modulation, thereby effectively mitigating desertification. Total terpenoids have been extracted from A. absinthium (AATP) and found to have antioxidant and anti-inflammatory activities. Terpenoids are a class of natural products derived from methyl hydroxypropanoic acid, for which their structural units consist of multiple isoprene (C5) units. They are one of the largest and most structurally diverse classes of natural compounds. However, there are still large gaps in knowledge regarding their exact biological activities and effects. Atherosclerosis (AS) is a prevalent cardiovascular disease marked by the chronic inflammation of the vascular system, and lipid metabolism plays a key role in its pathogenesis. This study determined the extraction and purification processes of AATP through single-factor experiments and response surface optimization methods. The purity of AATP was increased from 20.85% ± 0.94 before purification to 52.21% ± 0.75, which is 2.5 times higher than before purification. Studies have shown that the total terpenoids of A. absinthium significantly reduced four indices of serum lipids in atherosclerosis (AS) rats, thereby promoting lipid metabolism, inhibiting inflammatory processes, and hindering aortic wall thickening and hepatic fat accumulation. It is known from network pharmacology studies that AATP regulates the Janus kinase/signal transducer (JAK/STAT) signaling axis. Molecular docking studies have indicated that the active component of AATP effectively binds to Janus kinase (JAK2) and signal transducer (STAT3) target proteins. The results indicate that AATP can inhibit the release of pro-inflammatory mediators (such as reactive oxygen species (ROS)) in LPS-induced RAW264.7 macrophages. It also inhibits the M1 polarization of RAW264.7 macrophages. Protein immunoblotting analysis revealed that it significantly reduces the phosphorylation levels of Janus kinase (JAK2) and the signal transducer and activator of transcription 3 (STAT3). Research indicates that the active components in A. absinthium may exert anti-atherosclerotic effects by regulating lipid metabolism and inhibiting inflammatory responses. It holds potential value for development as a functional food or drug for the prevention and treatment of atherosclerosis. Full article

Virtual synchronous generator (VSG) control has been increasingly utilized for the grid integration of the voltage source inverter (VSI). Under large disturbances, such as voltage sags and grid faults, the VSG synchronization dynamic is highly nonlinear and cannot be evaluated by small-signal-based approaches. Conventionally, the equal area criterion (EAC) is utilized to analyze the transient stability of a synchronous machine or a VSG. However, it is found that the EAC is only valid under special scenarios when the damping effect is ignored. In this case, the EAC will provide conservative predictions and therefore put stringent requirements on the fault-clearing time. This paper reveals that the motion of a pendulum is essentially the same as the VSG swing equation. Due to this, the phase portrait approach, which was used to predict the pendulum motion, can be similarly applied for the VSG transient stability study. Based on the analogical phase portrait analysis, a damping coefficient tuning guideline is proposed, which always guarantees the synchronization stability as long as an equilibrium exists. The aforementioned theoretical findings are finally verified through a grid-connected VSG under the hardware-in-loop (HIL) environment. Full article

To enhance pulsar candidate recognition performance and improve model generalization, this paper proposes the cross-feature hybrid associative prior network (CFHAPNet). CFHAPNet incorporates a novel architecture and strategies to integrate multi-class heterogeneous feature subimages from each candidate into multi-channel data processing. By implementing cross-attention mechanisms and other enhancements for multi-view feature interactions, the model significantly strengthens its ability to capture fine-grained image texture details and weak prior semantic information. Through comparative analysis of feature weight similarity between subimages and average fusion weights, CFHAPNet efficiently identifies and filters genuine pulsar signals from candidate images collected across astronomical observatories. Additionally, refinements to the original loss function enhance convergence, further improving recognition accuracy and stability. To validate CFHAPNet’s efficacy, we compare its performance against several state-of-the-art methods on diverse datasets. The results demonstrate that under similar data scales, our approach achieves superior recognition performance. Notably, on the FAST dataset, the accuracy, recall, and F1-score reach 97.5%, 98.4%, and 98.0%, respectively. Ablation studies further reveal that the proposed enhancements improve overall recognition performance by approximately 5.6% compared to the original architecture, achieving an optimal balance between recognition precision and computational efficiency. These improvements make CFHAPNet a strong candidate for future large-scale pulsar surveys using new sensor systems. Full article

Electromagnetic interference (EMI) remains a difficult task in the design and operation of contemporary power electronic systems, especially in those applications where signal quality has a direct impact on the overall performance and efficiency. Conventional control schemes that have evolved to counteract the effects of EMI generally tend to have greater design complexity, greater error rates, poor control accuracy, and large amounts of harmonic distortion. In order to overcome these constraints, this paper introduces an intelligent and advanced control approach founded on the signal randomization principle. The suggested approach controls the switching activity of a DC–DC converter by dynamically tuned parameters like duty cycle, switching frequency, and signal modulation. A boost interleaved topology is utilized to maximize the current distribution and minimize ripple, and an innovative space vector-dithered sigma delta modulation (SV-DiSDM) scheme is proposed for cancelling harmonics via a digitalized control action. The used modulation scheme can effectively distribute the harmonic energy across a larger range of frequencies to largely eliminate EMI and boost the stability of the system. High-performance analysis is conducted by employing significant measures like total harmonic distortion (THD), switching frequency deviation, switching loss, and distortion product. Verification against conventional control models confirms the increased efficiency, less EMI, and greater signal integrity of the proposed method, and hence, it can be a viable alternative for EMI-aware power electronics applications. Full article

Background: Casein kinase 1 epsilon (CK1ε) plays a critical role in cancer progression by activating oncogenic signaling pathways, making it a target for cancer therapy. However, no inhibitors are currently available for clinical use, highlighting the need for novel therapeutic candidates. Methods: This study aimed to identify potential CK1ε inhibitors. To achieve this, a modified version of a previously reported pharmacophore model was applied to an ultra-large database of over 100 million compounds for virtual screening. Hits were filtered based on drug-likeness and pH-dependent pharmacophore compliance and then grouped according to their structural core. A representative compound from each structural group underwent molecular dynamic (MD) simulations and binding free energy calculations to predict its stability and affinity, allowing extrapolation of the results to the entire set of candidates. Results: Pharmacophore matching initially identified 290 compounds. After energy minimization, and an assessment of drug-likeness and pharmacophore compliance, we selected 29 structurally related candidates. MD simulations showed that most of the compounds representative of structural groups had stable binding modes, favorable intermolecular interactions, and free energies comparable to those of previously reported CK1ε inhibitors. An analysis of additional members of the most promising structural group showed that two 2,4-diaminopyrimidine-based compounds likely inhibit CK1ε. Conclusions: These findings provide structural insights into the design of CK1ε inhibitors, supporting compound optimization and the eventual development of targeted cancer therapeutics. Full article

To enhance the classification accuracy of the ResNet model for 12-lead ECG signals, a novel approach that focuses on optimizing the learning rate within the model training algorithm is proposed. Firstly, a Taylor expansion of the training formula for model weights is performed to derive a learning rate that incorporates the second-order gradient information. Subsequently, to circumvent the direct computation of the complex second-order gradient in the learning rate, an approximation method utilizing the historical first-order gradient is introduced. Additionally, truncation techniques are employed to ensure that the second-order learning rate remains neither excessively large nor too small. Ultimately, the 1D-ResNet-AdaSOM model is constructed based on this adaptive second-order momentum (AdaSOM) method and applied for 12-lead ECG classification. The proposed algorithm and model were validated on the CPSC2018 dataset. The evolving trend of the loss function throughout the training process demonstrated that the proposed algorithm exhibited commendable convergence and stability, and these results aligned with the conclusions derived from the theoretical analysis of the algorithm’s convergence. On the test set, the model attained an impressive average F1 score of 0.862, demonstrating that 1D-ResNet-AdaSOM surpassed several state-of-the-art deep-learning models in performance while exhibiting strong robustness. The experimental findings further substantiate our hypothesis that adjusting the learning rate in the ResNet training algorithm can effectively enhance classification accuracy for 12-lead ECGs. Full article