Impact of interdigitated electrodes design on the low frequency and random telegraph noise of single-layer graphene micro ribbons

Investigating the potential of exploiting the missing fundamental phenomenon for low frequency noise control at music events

Abstract We present a miniaturized,fiber-free-space hybrid optical system designed for a spaceborne
 Integrating Sphere Cold-atom Clock (ISCAC).All optical components were integrated on
 both sides of a 20% SiC/Al composite bench,with dimensions of 220mm× 220mm×65.5mm,
 achieving remarkable compactness (3.17L volume) and lightweight characteristics (2.6kg
 mass).The laser source was split into six functional beams for cooling,probing,pumping,
 repumping,and two dedicated beams for modulation transfer spectroscopy.Comprehensive
 opto-mechanical-thermal co-simulations and experimental validation demonstrate robust
ness of the system under 10.26g RMS mechanical vibrations, simulating satellite launch
 conditions,with output power variations maintained below 2%.Furthermore,the system ex
hibited high thermal stability during temperature cycling (−5◦C to 45◦C),with fiber cou
pling efficiency variations constrained within 1% across all laser paths.Following power sta
bilization,the contribution of detection light relative intensity noise to ISCAC stability im
proved from 1.59 × 10−12τ−1/2 to 1.04 × 10−14τ−1/2.After frequency locking,the impact of
 laser frequency noise on frequency stability was reduced to 7.89 × 10−15τ−1/2.These results
 demonstrate that the laser system meets the stringent requirements for spaceborne cold
 atomic clocks.

Magnetic resonance (MR)-guided online adaptive radiation therapy (MRgOART) has attracted increasing attention owing to its capabilities to create daily adaptation plans and perform real-time motion management. However, during multileaf collimator (MLC) tracking, MRgOART systems face a latency of ∼300ms, which can compromise treatment accuracy. Therefore, predictive models for respiratory-induced liver motion are essential to compensate for system delays and enable precise MLC tracking. This study aims to develop a respiratory motion prediction model based on non-stationary transformers (NsTransformers) using cine MR images acquired using interleaved imaging on the Elekta Unity system. The performance of the proposed model is compared with that of iTransformers, bidirectional long short-term memory (LSTM) with an attention mechanism (biLSTM-ATT), LSTM and linear regression models to evaluate its potential for real-time clinical applications. Seventeen liver cancer patients treated with MRgOART were enrolled. Coronal and sagittal cine MR images were selected from three-plane interleaved images during free breathing or abdominal compression. Respiratory motion was defined as the displacement of the centroid position of the liver obtained via intensity-based deformable image registration. Data augmentation techniques, including random noise addition, amplitude modulation, and frequency transformation, were used to expand the training dataset. The NsTransformers model was trained to predict future centroid positions at forecasting horizons of 200, 400, and 600ms. The root mean square error (RMSE) and margin-based accuracy were used as evaluation metrics, and the statistical significance of the models was assessed using the Friedman and Nemenyi tests. The NsTransformers model consistently outperformed all comparative models across all forecast intervals. The RMSE results of the NsTransformers model were superior to those of the other models, with the NsTransformers model demonstrating statistically significant improvements (p<0.05) compared to the other models. In addition, the NsTransformers model achieved a higher margin-based accuracy across multiple prediction margins. The computation time of the NsTransformers model was ∼5ms per prediction, which is sufficiently short for real-time applications. However, the prediction accuracy degraded under conditions of irregular respiratory motion. A motion prediction model based on NsTransformers that effectively predicts respiratory-induced liver motion from interleaved cine MR images was developed. The model demonstrated superior prediction accuracy compared with all comparative models and holds promise in compensating for latency in MRgOART MLC tracking.

Open-circuit faults (OCFs) in three-level neutral-point-clamped (NPC) inverters can severely degrade power quality and compromise system reliability. However, existing diagnostic methods often exhibit performance degradation under mixed operating conditions and strong noise, and they remain highly sensitive to hyperparameter settings. To address these issues, this paper proposes an integrated model-optimization framework that couples a lightweight diagnostic network, DR-SE-NPCNet, with an improved honey Badger algorithm (IHBA) for global hyperparameter tuning. DR-SE-NPCNet preserves full temporal resolution through a temporal resolution preserving-temporal dilation (TRP-TD) backbone and enhances discriminative representations using a residual and squeeze-and-excitation-calibrated fusion (ReSE-CF) module. IHBA further stabilizes and improves the model by enabling efficient and robust hyperparameter optimization. Experiments on a hardware NPC inverter platform demonstrate that the proposed method achieves 92.83-96.94% accuracy under 10 dB noise and mixed variations in load level, modulation index, DC-bus voltage, and output frequency, outperforming conventional CNN-based approaches. With IHBA optimization, diagnostic accuracy is further increased by an additional 2-3%. These results confirm that the integrated DR-SE-NPCNet and IHBA framework provides a robust and high-accuracy solution for OCF diagnosis under severe noise and mixed operating conditions.

The impact of obstacle parameters on mid-high frequency noise propagation in comprehensive mining workfaces and safety implications.

Single-frequency AlGaInP-based VECSELs are high-brightness, low-noise lasers with capacity for compact power scaling, making them excellent candidates for quantum technology applications based on cold strontium (Sr) atoms, which require high-power, low-noise light at 689 nm. We demonstrate lateral power-scaling of an AlGaInP-VECSEL, producing multimode output power of 2 W around 689 nm and 1 W single-frequency at the Sr 1 S 0 → 3 P 1 cooling transition frequency (434.82 THz) using intracavity filtering. Relative to previous reports, this system offers an increase in multimode output power of &gt;350%. The thermal resistance of the system is estimated as 4.3(1) K/W, consistent with other AlGaInP-VECSEL reports. Single-frequency output power and wavelength stability are observed as 4.3% and 445 fm (280 MHz) rms over a 1-hour period, respectively. The single-frequency VECSEL was also locked to a Fabry-Perot reference cavity, demonstrating ultra-low relative intensity noise (RIN) of &lt;-152.9 dBc/Hz for frequencies above 1 kHz. Further frequency noise measurements resulted in estimated free-running and locked linewidths of 71(1) kHz (0.02 s) and 7.26(1) kHz (20 s), respectively, demonstrating suitability for high-power quantum applications based on cold Sr.

Sperm whale acoustic ecology around two sub-Antarctic islands.

The mercury ion microwave clock is considered to be one of the leading candidates for new generation satellite navigation atomic clocks due to its excellent long-term frequency stability, extremely low frequency drift rate, high reliability and good potential for miniaturization. In recent years, the performance and maturity of the space mercury ion microwave clock have improved rapidly, and the reported long-term stability results are mostly in low 10− 15 level. In this article, we present a compact laboratory prototype of mercury ion microwave clock aiming to spaceborne applications with the long-term stability below 1 × 10− 15. By regulating the physical effects contributing to the clock transition frequency shifts, the clock maintains a white frequency noise Allan deviation of 2.8 × 10− 13/τ1/2 with the averaging time τ over 105 s and achieves the long-term stability of 6.3 × 10− 16 for averaging times of 200,000 s. The space mercury ion microwave clock with such level performance will benefit the Global Navigation Satellite Systems and a wide range of space science and missions.

Electrostatic monitoring technology for aero-engines has demonstrated considerable capability in early fault warning. However, raw electrostatic signals often contain significant noise and exhibit low signal-to-noise ratios, making denoising essential to improve the accuracy of fault-related information extraction. To address the issue of coupled noise in electrostatic signals, this study introduces methodologies based on Improved Complete Ensemble EMD with Adaptive Noise (ICEEMDAN), Autocorrelation Function (ACF), and Wavelet Soft-thresholding (WTD). We investigate the criteria and principles for screening intrinsic mode functions (IMFs) and propose a joint denoising algorithm, along with its specific procedure, based on IMF-optimized reconstruction and wavelet thresholding. The proposed method is validated using both simulated signals and actual electrostatic signals collected from a micro-turbojet engine test. Comparisons with other denoising techniques are conducted. Simulation results indicate that the proposed method improves denoising performance in terms of signal-to-noise ratio (SNR), mean square error (MSE), and normalized cross-correlation (NCC). Test results further demonstrate that the method effectively suppresses random noise and power frequency interference while preserving useful abnormal particle signals more effectively.

Photoacoustic spectroscopy is a promising method for detecting dissolved acetylene (C2H2) in transformer oil, facilitating early fault diagnosis in power transformers. However, temperature variations significantly influence the resonance frequency of the photoacoustic cell, potentially reducing detection accuracy. This study investigates the temperature effects on the first-order longitudinal acoustic mode of a resonant photoacoustic cell using finite element simulations with thermo-viscous acoustics. The results show that as the temperature increases, the resonant frequency increases linearly and the sound pressure amplitude decreases, consistent with analytical models. To enhance system robustness, a perturbation observation method is proposed, treating operating frequency as the independent variable and acoustic pressure as the dependent variable. Time-domain simulations validate its effectiveness in tracking resonance frequency shifts under varying temperatures, ensuring reliable detection. Future work should focus on improving frequency resolution, noise filtering, and adaptive step-size optimization for practical applications.

Abstract Solid-state qubits are sensitive to their microscopic environment, causing the qubit properties to fluctuate on a wide range of timescales. The sub-Hz end of the spectrum is usually dealt with by repeated background calibrations, which bring considerable overhead. It is thus important to characterize and understand the low-frequency variations of the relevant qubit characteristics. In this study, we investigate the stability of spin qubit frequencies in the Si/SiGe quantum dot platform. We find that the calibrated qubit frequencies of a six-qubit device vary by up to ±100 MHz while performing a variety of experiments over a span of 912 days. These variations are sensitive to the precise voltage settings of the gate electrodes, however when these are kept constant to within 15 µ V, the qubit frequencies vary by less than ±7 MHz over periods up to 36 days. During overnight scans, the qubit frequencies of ten qubits across two different devices show a standard deviation below 200 kHz within a 1-hour time window. The qubit frequency noise spectral density shows roughly a 1 /f trend above 10 −4 Hz and, strikingly, a steeper trend at even lower frequencies.

The comparison between multiple linear regression and random forest model in predicting environmental noise and its frequency components level in Hong Kong using a land-use regression approach.

Abstract Benefiting from the ability to efficiently attenuate elastic waves or sound waves within specific frequency ranges, locally resonant phononic crystals (LRPC) have emerged as cutting-edge materials for achieving both low frequency vibration control and noise reduction. The bandgap, as a key characteristic of LRPC, is determined by the complex coupling of multiple parameters. To address the systemic shortcomings in existing bandgap parameter analysis and the difficulty in quantifying interaction effects, this study proposes a systematic multi-parameter sensitivity analysis framework integrating the Morris screening method with the response surface methology. Taking a typical 2D three-component LRPC as the study object, the comprehensive effects of multiple parameters on the locally resonant bandgap is thoroughly investigated. Foremost, an in-depth evaluation of the locally resonant bandgap mechanism was undertaken with the improved plane wave expansion method (IPWEM) and the finite element method (FEM). Subsequently, the Morris screening method was employed to comprehensively screen multiple structural and material parameters, rapidly identifying key bandgap-sensitive parameters to achieve effective dimensionality reduction in the parameter space. These critical bandgap-sensitive parameters include three structural parameters and four material parameters. Next, response surface methodology was employed to perform detailed multiple regression modeling of the relationship between the output response (bandgap characteristics) and input variables (key sensitive parameters). Through variance analysis and response surface visualization, the main effects and interaction effects of each key sensitive parameter on bandgap characteristics were precisely quantified. Results indicate that the scatterer radius, coating radius, matrix lattice constant, and matrix density exhibit meaningful interactions in determining bandgap characteristics. The vibration transmission calculation further demonstrates that the finite LRPC structure can achieve considerable attenuation of elastic waves excited by external sources within the bandgap.

Differential impacts of co-exposures to ELF-EMFs and noise on prostate-specific antigen levels: A longitudinal study.

High-stability underwater frequency transmission based on low noise frequency doubled green laser

ABSTRACT The outer Apulian Foreland ramp, i.e., the outer slope of the Taranto Trench is affected by submarine landslides, which may represent a geological hazard for the Ionian coastal area of Apulia. One of the major landslides is reported in the offshore Taranto, with evidence detectable in the vintage seismic reflection lines available for free. These are unmigrated and staked seismic reflection profiles as low‐resolution PDF raster images, making challenging their interpretation. The main goal of the present paper is the building of a dataset of these seismic reflection profiles, processed and improved, useful to whom interested in future investigation of this landslide area. Therefore, F75‐85, F75‐83, F75‐44, F75‐42, MT‐457‐85 and D‐482 seismic reflection profiles were transformed to SEG‐Y file. We first converted the PDF files in TIFF ones; these files, accompanied by related files in TXT format consisting of code rows, were transformed by the use of MATLAB program IMAGE2SEGY. Subsequently, the obtained SEG‐Y seismic images were enhanced by a light processing consisting in the removing the low frequency noise in DELPH Seismic software ambient. To complete the propaedeutic dataset to investigate this submarine landslide, the digitalisation of the PDF raster image of the sonic log belonging to the exploration well Sansone‐1 was performed. A CSV file was obtained after manual picking using WebPlotDigitizer. These data will allow to calculate the average velocity of the seismic P‐wave related to the lithostratigraphic units in the exploration well and, finally, to carry out the correlation between these units and the seismostratigraphic facies within the SEG‐Y reflection seismic sections.

Abstract The Laser Interferometer Space Antenna (LISA) will be a space-borne gravitational wave observatory that consists of three spacecraft, separated by several million kilometers, which tracks the separation between inertial test masses via laser interferometry. In this architecture strict requirements exist on the design of the orbits, the ability to accommodate laser frequency noise, the ability to provide the necessary purity of free-fall, and the quality of the optical metrology. This final item is enabled with afocal transmitting/receiving telescopes that increase the laser power transfer efficiency over the long inter-spacecraft link. These telescopes must be designed and built not to adversely affect the precision of the interferometric measurements. The function, design, and current status of LISA telescopes under development at NASA will be discussed in this article.

Complex organic molecules (COMs) are found to be abundant in various astrophysical environments, particularly toward star-forming regions, where they are observed both toward protostellar envelopes as well as shocked regions. The emission spectrum, especially that of heavier COMs, might consist of up to hundreds of lines, where line blending hinders the analysis. However, identifying the molecular composition of the gas that leads to the observed millimeter spectra is the first step toward a quantitative analysis. We have developed a new method based on supervised machine learning to recognize spectroscopic features of the rotational spectrum of molecules in the 3 mm atmospheric transmission band for a list of species including COMs, with the aim of obtaining a detection probability. We used local thermodynamic equilibrium (LTE) modeling to build a large set of synthetic spectra of 20 molecular species, including COMs with a range of physical conditions typical for star-forming regions. We successfully designed and trained a convolutional neural network (CNN) that provides detection probabilities of individual species in the spectra. We demonstrate that the CNN model we developed has a robust performance to detect spectroscopic signatures from these species in synthetic spectra. We evaluated its ability to detect molecules according to the noise level, frequency coverage, and line-richness, as well as to test its performance for an incomplete frequency coverage with high detection probabilities for the tested parameter space, with no false predictions. Finally, we applied the CNN model to obtain predictions on observational data from the literature toward line-rich hot core-like sources, where the detection probabilities remain reasonable, with no false detections. We demonstrate the use of CNNs in facilitating the analysis of complex millimeter spectra both on synthetic spectra, along with the first tests performed on observational data. Further analyses on its explainability, as well as calibration using a larger observational dataset, will help improve the performance of our method for future applications.

We report on the experimental analysis of the statistics of the optical phase of the light emitted by a distributed-feedback (DFB) laser. We measure the dependence of the phase diffusion coefficient on the applied bias current in the region around the threshold current. We measure the phase noise statistics using three different methods based on i) averages of the noise phase trajectories over different temporal windows, ii) the variance of the phase noise difference, and iii) the frequency noise spectral density. The comparison between the results obtained with these methods is succesful. We obtain that the phase diffusion coefficient monotonously decreases with the bias current in the region around threshold. We finally compare our experimental results with simple analytical expressions for the below and above threshold cases based on Schawlow-Townes laws.