Search Results (138)

Indirect detection phase control algorithms, such as the dithering algorithm and the stochastic parallel gradient descent algorithm (SPGD), have simple system structures and are applicable to filled-aperture coherent combinations with high efficiency. The performances of these algorithms are limited when applied to a coherent combination of pulsed fiber lasers with a low repetition rate (≤5 kHz). Firstly, due to the overlap of the phase noise frequency and repetition rate, conventional algorithms cannot effectively distinguish the phase noise from the pulse fluctuation, and directly applying filtering will result in the phase information being filtered out. Secondly, if the pulse fluctuation is ignored and only the continuous part of the phase information is utilized, it relies on the presetting of conditions to separate the pulse from the continuous part and loses the phase information of the pulse part. In this article, we propose a waveform self-referencing algorithm (WSRA) based on a two-channel near-infrared laser coherent combination system to overcome the above challenges. Firstly, by modelling a self-referencing waveform, a nonlinear scaling operation is performed on the combined signal to generate a pseudo-continuous signal, which removes the intrinsic pulse fluctuation while preserving the phase noise information. Secondly, the phase control signal is calculated based on the pseudo-continuous signals after parallel perturbation. Finally, the phase noise is corrected by optimization. The results show that our method effectively suppresses the waveform fluctuation at a 5 kHz repetition rate, the light intensity reaches an ideal value (0.9939 $I_{\max }$), and the root-mean-square (RMS) phase error is only 0.0130 $\lambda$. This method does not require the presetting of pulse detection thresholds or conditions, and solves the challenge of coherent combination at a low repetition rate, with adaptability to different pulse waveforms. Full article

In this study, the key challenges faced by global navigation satellite systems (GNSSs) in the field of security are addressed, and an eye diagram-based deep learning framework for intelligent classification of interference types is proposed. GNSS signals are first transformed into two-dimensional eye diagrams, enabling a novel visual representation wherein interference types are distinguished through entropy-centric feature analysis. Specifically, the quantification of information entropy within these diagrams serves as a theoretical foundation for extracting salient discriminative features, reflecting the structural complexity and uncertainty of the underlying signal distortions. We designed a hybrid architecture that integrates spatial feature extraction, gradient stability enhancement, and time dynamics modeling capabilities and combines the advantages of a convolutional neural network, residual network, and bidirectional long short-term memory network. To further improve model performance, we propose an improved coati optimization algorithm (ICOA), which combines chaotic mapping, an elite perturbation mechanism, and an adaptive weighting strategy for hyperparameter optimization. Compared with mainstream optimization methods, this algorithm improves the convergence accuracy by more than 30%. Experimental results on jamming datasets (continuous wave interference, chirp interference, pulse interference, frequency-modulated interference, amplitude-modulated interference, and spoofing interference) demonstrate that our method achieved performance in terms of accuracy, precision, recall, F1 score, and specificity, with values of 98.02%, 97.09%, 97.24%, 97.14%, and 99.65%, respectively, which represent improvements of 1.98%, 2.80%, 6.10%, 4.59%, and 0.33% over the next-best model. This study provides an efficient, entropy-aware, intelligent, and practically feasible solution for GNSS interference identification. Full article

Underground blasting vibrations are a critical factor influencing the stability of mine slopes. However, existing studies have yet to establish a quantitative relationship or clarify the underlying mechanisms linking blasting-induced vibrations and slope deformation. Taking the Shilu Iron Mine as a case study, this research develops a dynamic mechanical response model of slope stability that accounts for blasting loads. By integrating slope radar remote sensing data and applying the Pearson correlation coefficient, this study quantitatively evaluates—for the first time—the correlation between underground blasting activity and slope surface deformation. The results reveal that blasting vibrations are characterized by typical short-duration, high-amplitude pulse patterns, with horizontal shear stress identified as the primary trigger for slope shear failure. Both elevation and lithological conditions significantly influence the intensity of vibration responses: high-elevation areas and structurally loose rock masses exhibit greater dynamic sensitivity. A pronounced lag effect in slope deformation was observed following blasting, with cumulative displacements increasing by 10.13% and 34.06% at one and six hours post-blasting, respectively, showing a progressive intensification over time. Mechanistically, the impact of blasting on slope stability operates through three interrelated processes: abrupt perturbations in the stress environment, stress redistribution due to rock mass deformation, and the long-term accumulation of fatigue-induced damage. This integrated approach provides new insights into slope behavior under blasting disturbances and offers valuable guidance for slope stability assessment and hazard mitigation. Full article

Cumulative inhibition of voltage-gated Na⁺ channel current (I_Na) caused by high-frequency depolarization plays a critical role in regulating electrical activity in excitable cells. As discussed in this review paper, exposure to certain small-molecule modulators can perturb I_Na during high-frequency stimulation, influencing the extent of cumulative inhibition and electrical excitability in excitable cells. Carbamazepine differentially suppressed transient or peak (I_Na(T)) and late (I_Na(L)) components of I_Na. Moreover, the cumulative inhibition of I_Na(T) during pulse-train stimulation at 40 Hz was enhanced by lacosamide. GV-58 was noted to exert stimulatory effect on I_Na(T) and I_Na(L). This stimulated I_Na was not countered by ω-conotoxin MVIID but was effectively reversed by ranolazine. GV-58′s exposure can slow down I_Na inactivation elicited during pulse-train stimulation. Lacosamide directly inhibited I_Na magnitude as well as promoted this cumulative inhibition of I_Na during pulse-train stimuli. Mirogabalin depressed I_Na magnitude as well as modulated frequency dependence of the current. Phenobarbital can directly modulate both the magnitude and frequency dependence of ionic currents, including I_Na. Previous investigations have shown that exposure to small-molecule modulators can perturb I_Na under conditions of high-frequency stimulation. This ionic mechanism plays a crucial role in modulating membrane excitability, hereby supporting the validity of these findings. Full article

A new approach in amperometric enzyme electrodes production based on all-electrochemically assisted procedures will be described. Enzyme (glucose oxidase) immobilization was performed by in situ co-crosslinking of enzyme molecules through electrophoretic protein deposition, assuring enzyme immobilization exclusively onto the transducer surface (Pt electrode). Analogously, the poor selectivity of the transducer was dramatically improved by the electrosynthesis of non-conducting polymers with built-in permselectivity, permitting the formation of a thin permselective film onto the transducer surface, able to reject common interferents usually found in real samples. Since both approaches required a proper and distinct electrochemical perturbation (a pulsed current sequence for electrophoretic protein deposition and cyclic voltammetry for the electrosynthesis of non-conducting polymers), an appropriate coupling of the two all-electrochemical approaches was assured by a thorough study of the likely combinations of the electrosynthesis of permselective polymers with enzyme immobilization by electrophoretic protein deposition and by the use of several electrosynthesized polymers. For each investigated combination and for each polymer, the analytical performances and the rejection capabilities of the resulting biosensor were acquired so to gain information about their sensing abilities eventually in real sample analysis. This study shows that the proper coupling of the two all-electrochemical approaches and the appropriate choice of the electrosynthesized, permselective polymer permits the easy fabrication of novel glucose oxidase biosensors with good analytical performance and low bias in glucose measurement from typical interferent in serum. This novel approach, resembling classical electroplating procedures, is expected to allow all the advantages expected from such procedures like an easy preparation biosensor, a bi-dimensional control of enzyme immobilization and thickness, interferent- and fouling-free transduction of the electrodic sensor and, last but not the least, possibility of miniaturization of the biosensing device. Full article

The objective of the present study is to detect chirped optical solitons of the perturbed Chen–Lee–Liu equation with full nonlinearity. By virtue of the traveling wave hypothesis, the discussed model is reduced to a simple form known as an elliptic equation. The latter equation, which is a second-order ordinary differential equation, is handled by the undetermined coefficient method of two forms expressed in terms of the hyperbolic secant and tangent functions. Additionally, the auxiliary equation method is applied to derive several miscellaneous solutions. Various types of chirped solitons are revealed such as W-shaped, bright, dark, gray, kink and anti-kink waves. Taking into consideration the existence conditions, the dynamical behaviors of optical solitons and their corresponding chirp are illustrated. The modulation instability of the perturbed CLL equation is examined by means of the linear stability analysis. It is found that all solutions are stable against small perturbations. These entirely new results, compared to previous works, can be employed to understand pulse propagation in optical fiber mediums and dynamic characteristics of waves in plasma. Full article

This paper presents an adaptive Maximum Power Point Tracking (MPPT) strategy for grid-connected photovoltaic (PV) systems that uses an Adaptive Neuro-Fuzzy Inference System (ANFIS) optimized by Particle Swarm Optimization (PSO) to enhance energy extraction efficiency under diverse environmental conditions. The proposed ANFIS-PSO-based MPPT controller performs dynamic adjustment Pulse Width Modulation (PWM) switching to minimize Total Harmonic Distortion (THD); this will ensure rapid convergence to the maximum power point (MPP). Unlike conventional Perturb and Observe (P&O) and Incremental Conductance (INC) methods, which struggle with tracking delays and local maxima in partial shading scenarios, the proposed approach efficiently identifies the Global Maximum Power Point (GMPP), improving energy harvesting capabilities. Simulation results in MATLAB/Simulink R2023a demonstrate that under stable irradiance conditions (1000 W/m², 25 °C), the controller was able to achieve an MPPT efficiency of 99.2%, with THD reduced to 2.1%, ensuring grid compliance with IEEE 519 standards. In dynamic irradiance conditions, where sunlight varies linearly between 200 W/m² and 1000 W/m², the controller maintains an MPPT efficiency of 98.7%, with a response time of less than 200 ms, outperforming traditional MPPT algorithms. In the partial shading case, the proposed method effectively avoids local power maxima and successfully tracks the Global Maximum Power Point (GMPP), resulting in a power output of 138 W. In contrast, conventional techniques such as P&O and INC typically fail to escape local maxima under similar conditions, leading to significantly lower power output, often falling well below the true GMPP. This performance disparity underscores the superior tracking capability of the proposed ANFIS-PSO approach in complex irradiance scenarios, where traditional algorithms exhibit substantial energy loss due to their limited global search behavior. The novelty of this work lies in the integration of ANFIS with PSO optimization, enabling an intelligent self-adaptive MPPT strategy that enhances both tracking speed and accuracy while maintaining low computational complexity. This hybrid approach ensures real-time adaptation to environmental fluctuations, making it an optimal solution for grid-connected PV systems requiring high power quality and stability. The proposed controller significantly improves energy harvesting efficiency, minimizes grid disturbances, and enhances overall system robustness, demonstrating its potential for next-generation smart PV systems. Full article

A complex study was performed of the disturbances in geophysics parameters that were observed during a short-term period of earthquake preparation. On 4 March 2024, an $M6.1$ earthquake (N 42.93, E 76.966) occurred with the epicenter 12.2 km apart from the complex of geophysical monitoring. Preparation of the earthquake we detected in real time, 8 days prior to the main shock, when a characteristic cove-like decrease appeared in the gamma-ray flux measured 100 m below the surface of the ground, which observation indicated an approaching earthquake with high probability. Besides the gamma-ray flux, anomalies connected with the earthquake preparation were studied in the variation of the Earth’s natural pulsed electromagnetic field (ENPEMF) at very low frequencies (VLF) $f=7.5$ kHz and $f=10.0$ kHz and at ultra-low frequency (ULF) in the range of 0.001–20 Hz, as well as in the shift of Doppler frequency (DFS) of the ionospheric signal. A drop detected in DFS agrees well with the decrease in gamma radiation background. A sequence of disturbance appearance was revealed, first in the variations of ENPEMF in the VLF band and of the subsurface gamma-ray flux, both of which reflect the activation dynamic of tectonic processes in the lithosphere, and next in the variation of DFS. Two types of earthquake-connected effects may be responsible for the transmission of the perturbation from the lithosphere into the ionosphere: the ionizing gamma-ray flux and the ULF/VLF emission, as direct radiation from the nearby earthquake source. In the article, we emphasize the role of medium ionization in the propagation of seismogenic effects as a channel for realizing the lithosphere–ionosphere coupling. Full article

The extraction of adherent cells, such as B16 murine melanoma cells, from Petri dish cultures is critical in biomedical applications, including cell reprogramming, transplantation, and regenerative medicine. Traditional detachment methods—enzymatic, mechanical, or chemical—often compromise cell viability by altering membrane integrity and disrupting adhesion proteins. To address these challenges, this study investigated sensor-based detection of the pre-detachment phase in a MPWM (Microfluidic Pulse Width Modulation) extraction system. Our approach integrates a micromechatronic system with a microfluidic suction circuit, real-time CCD imaging, and computational analysis to detect and characterize the pre-detachment moment before full extraction. A precisely controlled hydrodynamic force field progressively disrupts adhesion in multiple stages, reducing mechanical stress and preserving cell integrity. Real-time video analysis enables continuous monitoring of positional dynamics and oscillatory responses. Image processing and deep learning algorithms determine object center coordinates, allowing the MPWM system to dynamically adjust suction parameters. This optimizes detachment while minimizing liquid absorption and reflux volume, ensuring efficient extraction. By combining microfluidics, sensor detection, and AI-driven image processing, this study established a non-invasive method for optimizing adherent cell detachment. These findings have significant implications for single-cell research, regenerative medicine, and high-throughput biotechnology, ensuring maximal viability and minimal perturbation. Full article

Fiber-optic Fabry–Pérot (F–P) sensors offer significant potential for non-invasive hemodynamic monitoring, but existing sensing systems face limitations in multi-channel measurement capabilities and dynamic demodulation accuracy. This study introduces a sparse-frequency-scanning white-light interferometry (SFS-WLI) system with an adaptive mode-locked cross-correlation (MLCC) algorithm to address these challenges. The system leverages telecom-grade semiconductor lasers (191.2–196.15 THz sweep range, 50 GHz step) and a Fibonacci-optimized MLCC algorithm to achieve real-time cavity length demodulation at 5 kHz. Compared to normal MLCC algorithm, the Fibonacci-optimized algorithm reduces the number of computational iterations by 57 times while maintaining sub-nanometer resolution under dynamic perturbations. Experimental validation demonstrated a carotid–radial pulse wave velocity of 5.12 m/s in a healthy male volunteer. This work provides a scalable and cost-effective solution for cardiovascular monitoring with potential applications in point-of-care testing (POCT) and telemedicine. Full article

The Coherent Hemodynamic Spectroscopy (CHS) model provides a quantitative framework for modeling cerebral hemodynamics and metabolism, particularly in response to small physiological perturbations. However, in its original approximate formulation it was limited to conditions where parameter changes were constrained to 10–20%, making it unsuitable for modeling extreme physiological disruptions such as cardiac arrest. In this study, we present a detailed discussion of the algorithm using the complete CHS model, which extends the original framework by solving partial differential equations without approximations to handle large non-periodic perturbations. This model was applied to data from a previously published cardiac arrest and cardiopulmonary resuscitation (CPR) study in pigs, where cerebral blood flow changed by 100%. While our prior work demonstrated the utility of this approach for analyzing cerebral microvascular and metabolic parameters, it did not include the algorithmic details necessary for reproducibility and broader application. Here, we address this gap by describing the algorithm’s workflow, including the use of non-linear multivariate optimization, and its ability to recover multiple physiological variables, such as the capillary and venule oxygen saturations, and parameters, such as the capillary oxygen diffusion rate, and arterial oxygen saturation. The latter can be valuable when the pulse oximetry measurements are unavailable due to unstable, weak or absent pulse. This study underscores the importance of non-linear modeling in advancing the application of CHS to extreme physiological conditions and highlights its potential for translational research and clinical innovation. Full article

Since the research of $\mu$-synchronization helps to explore how complex networks (CNs) work together to produce complex behaviors, the $\mu$-synchronization task for uncertain time-delayed CNs is studied in our work. Especially, bounded external perturbations and variable delays of unknown bounds containing coupling delays, internal delays, and pulse delays are all taken into consideration, making the model more general. Through the $\mu$-stable theory together with the hybrid impulsive control technique, the problems caused by uncertain inner couplings, time-varying delays, and perturbations can be solved, and novel synchronization criteria are gained for the $\mu$-synchronization of the considered CNs. Different from traditional models, it is not necessary for the coupling matrices to meet the zero-row-sum condition, and the control protocol relaxes the constraint of time delays on impulse intervals. Moreover, numerical experiments and image encryption algorithms are carried out to verify our theoretical results’ effectiveness. Full article

The core subunits of the K_V7.2, K_V7.3, and K_V7.5 channels, encoded by the KCNQ2, KCNQ3, and KCNQ5 genes, are expressed across various cell types and play a key role in generating the M-type K⁺ current (I_K(M)). This current is characterized by an activation threshold at low voltages and displays slow activation and deactivation kinetics. Variations in the amplitude and gating kinetics of I_K(M) can significantly influence membrane excitability. Notably, I_K(M) demonstrates distinct voltage-dependent hysteresis when subjected to prolonged isosceles-triangular ramp pulses. In this review, we explore various small-molecule modulators that can either inhibit or enhance the amplitude of I_K(M), along with their perturbations on its gating kinetics and voltage-dependent hysteresis. The inhibitors of I_K(M) highlighted here include bisoprolol, brivaracetam, cannabidiol, nalbuphine, phenobarbital, and remdesivir. Conversely, compounds such as flupirtine, kynurenic acid, naringenin, QO-58, and solifenacin have been shown to enhance I_K(M). These modulators show potential as pharmacological or therapeutic strategies for treating certain disorders linked to gain-of-function or loss-of-function mutations in M-type K⁺ (K_V7x or KCNQx) channels. Full article

Background: Ischemic heart disease (IHD) impacts the quality of life and is the most frequently reported cause of morbidity and mortality globally. Aims: To assess the changes in the exhaled volatile organic compounds (VOCs) in patients with vs. without ischemic heart disease (IHD) confirmed by stress computed tomography myocardial perfusion (CTP) imaging. Objectives: IHD early diagnosis and management remain underestimated due to the poor diagnostic and therapeutic strategies including the primary prevention methods. Materials and Methods: A single center observational study included 80 participants. The participants were aged ≥ 40 years and given an informed written consent to participate in the study and publish any associated figures. Both groups, G1 (n = 31) with and G2 (n = 49) without post stress-induced myocardial perfusion defect, passed cardiologist consultation, anthropometric measurements, blood pressure and pulse rate measurements, echocardiography, real time breathing at rest into PTR-TOF-MS-1000, cardio-ankle vascular index, bicycle ergometry, and immediately after performing bicycle ergometry repeating the breathing analysis into the PTR-TOF-MS-1000, and after three minutes from the end of the second breath, repeat the breath into the PTR-TOF-MS-1000, then performing CTP. LASSO regression with nested cross-validation was used to find the association between the exhaled VOCs and existence of myocardial perfusion defect. Statistical processing performed with R programming language v4.2 and Python v.3.10 [^R], STATISTICA program v.12, and IBM SPSS v.28. Results: The VOCs specificity 77.6% [95% confidence interval (CI); 0.666; 0.889], sensitivity 83.9% [95% CI; 0.692; 0.964], and diagnostic accuracy; area under the curve (AUC) 83.8% [95% CI; 0.73655857; 0.91493173]. Whereas the AUC of the bicycle ergometry 50.7% [95% CI; 0.388; 0.625], specificity 53.1% [95% CI; 0.392; 0.673], and sensitivity 48.4% [95% CI; 0.306; 0.657]. Conclusions: The VOCs analysis appear to discriminate individuals with vs. without IHD using machine learning models. Other: The exhaled breath analysis reflects the myocardiocytes metabolomic signature and related intercellular homeostasis changes and regulation perturbances. Exhaled breath analysis poses a promise result to improve the diagnostic accuracy of the physical stress tests using machine learning models. Full article

Acoustic noise is known to perturb reading for good readers, including children and adults. This external acoustic noise interfering at the multimodal areas in the brain causes difficulties reducing reading and writing performances. Moreover, it is known that people with developmental coordination disorder (DCD) and dyslexia have reading deficits even in the absence of acoustic noise. The goal of this study is to investigate the effects of additional acoustic noise on an adult with DCD and dyslexia. Indeed, as vision is the main source of information for the brain during reading, a noisy internal visual crowding has been observed in many cases of readers with dyslexia, as additional mirror or duplicated images of words are perceived by these observers, simultaneously with the primary images. Here, we show that when the noisy internal visual crowding and an increasing external acoustic noise are superimposed, a reading disruptive threshold at about 50 to 60 dBa of noise is reached, depending on the type of acoustic noise for a young adult with DCD and dyslexia but not for a control. More interestingly, we report that this disruptive noise threshold can be controlled by Hebbian mechanisms linked to a pulse-modulated lighting that erases the confusing internal crowding images. An improvement of 12 dBa in the disruptive threshold is then observed with two types of acoustic noises, showing the potential utility of Hebbian optocontrol in managing reading difficulties in adults with DCD and dyslexia. Full article