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We present an efficient real space formalism for hybrid exchange-correlation functionals in generalized Kohn–Sham density functional theory (DFT). In particular, we develop an efficient representation for any function of the real space finite-difference Laplacian matrix by leveraging its Kronecker product structure, thereby enabling the time to solution of associated linear systems to be highly competitive with the fast Fourier transform scheme while not imposing any restrictions on the boundary conditions. We implement this formalism for both the unscreened and range-separated variants of hybrid functionals. We verify its accuracy and efficiency through comparisons with established planewave codes for isolated as well as bulk systems. In particular, we demonstrate up to an order-of-magnitude speedup in time to solution for the real space method. We also apply the framework to study the structure of liquid water using ab initio molecular dynamics, where we find good agreement with the literature. Overall, the current formalism provides an avenue for efficient real-space DFT calculations with hybrid density functionals. [ABSTRACT FROM AUTHOR]

This article presents an innovative predictive maintenance for grinding mills, aiming to enhance operational efficiency and minimize downtime. Leveraging advancements in data analytics and IoT sensor technologies, the approach integrates vibration signal forecasting, LSTM-based fast Fourier transform (FFT) analysis, and convolutional neural networks (CNNs) to detect faults early on. The method involves creating LSTM models to forecast vibration signals based on historical data and using FFT analysis to identify fault frequencies associated with the grinding process. Additionally, techniques such as Mel-frequency cepstral coefficients (MFCCs), short-time Fourier transform (STFT), and continuous wavelet transform (CWT) are employed for spectrogram extraction, providing valuable insights into machinery conditions. Validation on real-world datasets with 99.95% of accuracy with 1 in AUC-ROC, showcases the robust predictive performance of the model and has reached 99.96% of accuracy for 16 classes with an AUC-ROC of 1 using CWRU dataset, surpassing existing approaches and demonstrating its potential for proactive maintenance across various industries beyond mining. [ABSTRACT FROM AUTHOR]

Diffusion models are increasingly applied in low‐light image enhancement tasks due to their exceptional capability to model data distributions, but most current methods focus only on the original pixel space and neglect the potential of Fourier frequency information. In this article, SFDiff is proposed, a novel low‐light image enhancement method that integrates Fourier frequency information into the diffusion process. Specifically, Fourier transforms are applied at both the image and feature levels to separately enhance the amplitude and phase components, which restores global illumination degradation and positional information. Then a Spatial‐Frequency Fusion (SFF) block is used to fully integrate and interact with the information across spatial and frequency domains. Since illumination degradation is primarily manifested in the amplitude component, a loss function based on maximum likelihood learning is employed to constrain the amplitude component at each step of the sampling process, ensuring that the reverse process maintains an optimal trajectory. Owing to the streamlined network design and the fact that the Fourier transform requires no extra parameters, SFDiff achieves a reduction in parameters of over 35%$35\%$ compared to several state‐of‐the‐art (SOTA) diffusion models and delivers high‐quality enhancement results on multiple real‐world datasets. The code is available at https://github.com/MrWan001/SFDiff. [ABSTRACT FROM AUTHOR]

Imposing nonperiodic boundary conditions for unit cell analyses may be necessary for a number of reasons in applications, for example, for validation purposes and specific computational setups. The work at hand discusses a strategy for utilizing the powerful technology behind fast Fourier transform (FFT)‐based computational micromechanics—initially developed with periodic boundary conditions in mind—for essential boundary conditions in mechanics, as well, for the case of the discretization on a rotated staggered grid. Introduced by F. Willot into the community, the rotated staggered grid is presumably the most popular discretization, and was shown to be equivalent to underintegrated trilinear hexahedral elements. We leverage insights from previous work on the Moulinec–Suquet discretization, exploiting a finite‐strain preconditioner for small‐strain problems and utilize specific discrete sine and cosine transforms. We demonstrate the computational performance of the novel scheme by dedicated numerical experiments and compare displacement‐based methods to implementations on the deformation gradient. [ABSTRACT FROM AUTHOR]

Spurred by the simultaneous need for data privacy protection and data sharing, federated learning (FL) has been proposed. However, it still poses a risk of privacy leakage in it. This paper, an improved Differential Privacy (DP) algorithm to protect the federated learning model. Additionally, the Fast Fourier Transform (FFT) is used in the computation of the privacy budget , to minimize the impact of limited arithmetic resources and numerous users on the effectiveness of training model. Moreover, instead of direct analyses of the privacy budget through various methods, Privacy Loss Distribution (PLD) and privacy curves are adopted, while the number of artificial assignments hyperparameters is reduced, and the grid parameters delineated for FFT use are improved. The improved algorithm tightens parameter bounds and minimizes human factors' influence with minimal efficiency impact. It decreases the errors caused by truncation and discreteness of PLDs while expanding the discreteness interval to reduce the calculation workload. Furthermore, an improved activation function using a temper sigmoid with only one parameter , smooths the accuracy curve and mitigates drastically fluctuating scenarios during model training. Finally, simulation results on real datasets show that our improved DP algorithm, which accounts for long trailing, facilitates a better balance between privacy and utility in federated learning models. [ABSTRACT FROM AUTHOR]

The article discusses the discovery of active sound production in the wild Mediterranean blonde ray Raja brachyura, a type of elasmobranch. The sound produced by the blonde ray consists of a series of high-pitched pulses and is similar to sounds produced by other elasmobranch species. This finding sheds light on the communication and behavioral aspects of elasmobranchs, highlighting the importance of further research in this area. [Extracted from the article]

Traffic shockwaves demonstrate the formation and spreading of traffic fluctuation on roads. Existing methods mainly detect the shockwaves and their propagation by estimating traffic density and flow, which presents weaknesses in applications when traffic data is only partially or locally collected. This paper proposed a four-step data-driven approach that integrates machine learning with the traffic features to detect shockwaves and estimate their propagation speeds only using partial vehicle trajectory data. Specifically, we first denoise the speed data derived from trajectory data by the Fast Fourier Transform (FFT) to mitigate the effect of spontaneous random speed fluctuation. Next, we identify trajectory curves' turning points where a vehicle runs into a shockwave and its speed presents a high standard deviation within a short interval. Furthermore, the Density-based Spatial Clustering of Applications with Noise algorithm (DBSCAN) combined with traffic flow features is adopted to split the turning points into different clusters, each corresponding to a shockwave with constant speed. Last, the one-norm distance regression method is used to estimate the propagation speed of detected shockwaves. The proposed framework was applied to the field data collected from the I-80 and US-101 freeway by the Next Generation Simulation (NGSIM) program. The results show that this four-step data-driven method could efficiently detect the shockwaves and their propagation speeds without estimating the traffic densities and flows nearby. It performs well for both homogenous and nonhomogeneous road segments with trajectory data collected from total or partial traffic flow. [ABSTRACT FROM AUTHOR]

Algebraic multigrid (AMG) is one of the most efficient iterative methods for solving large structured systems of equations. However, how to build/check restriction and prolongation operators in practical AMG methods for nonsymmetric structured systems is still an interesting open question in its full generality. The present paper deals with the block-structured dense and Toeplitz-like-plus-cross systems, including nonsymmetric indefinite and symmetric positive definite (SPD) ones, arising from nonlocal diffusion problems. The simple (traditional) restriction operator and prolongation operator are employed in order to handle such block-structured dense and Toeplitz-like-plus-cross systems, which are convenient and efficient when employing a fast AMG. We provide a detailed proof of the two-grid convergence of the method for the considered SPD structures. The numerical experiments are performed in order to verify the convergence with a computational cost of only O (N log N) arithmetic operations, by exploiting the fast Fourier transform, where N is the number of the grid points. To the best of our knowledge, this is the first contribution regarding Toeplitz-like-plus-cross linear systems solved by means of a fast AMG. [ABSTRACT FROM AUTHOR]

We discuss how Dirichlet boundary conditions can be directly imposed for the Moulinec–Suquet discretization on the boundary of rectangular domains in iterative schemes based on the fast Fourier transform (FFT) and computational homogenization problems in mechanics. Classically, computational homogenization methods based on the fast Fourier transform work with periodic boundary conditions. There are applications, however, when Dirichlet (or Neumann) boundary conditions are required. For thermal homogenization problems, it is straightforward to impose such boundary conditions by using discrete sine (and cosine) transforms instead of the FFT. This approach, however, is not readily extended to mechanical problems due to the appearance of mixed derivatives in the Lamé operator of elasticity. Thus, Dirichlet boundary conditions are typically imposed either by using Lagrange multipliers or a "buffer zone" with a high stiffness. Both strategies lead to formulations which do not share the computational advantages of the original FFT-based schemes. The work at hand introduces a technique for imposing Dirichlet boundary conditions directly without the need for indefinite systems. We use a formulation on the deformation gradient—also at small strains—and employ the Green's operator associated to the vector Laplacian. Then, we develop the Moulinec–Suquet discretization for Dirichlet boundary conditions—requiring carefully selected weights at boundary points—and discuss the seamless integration into existing FFT-based computational homogenization codes based on dedicated discrete sine/cosine transforms. The article culminates with a series of well-chosen numerical examples demonstrating the capabilities of the introduced technology. [ABSTRACT FROM AUTHOR]

Unexpected faults in rotating machinery can lead to cascading disruptions of the entire work process, emphasizing the importance of early detection of performance degradation and identification of the current state. To accurately assess the health of a machine, this study introduces an FFT‐based raw vibration data preprocessing and graph representation technique, which analyses changes in frequency bands to detect early degradation trends in vibration data that may appear normal. The approach proposes a methodology that utilizes a graph convolutional autoencoder trained using only normal data to extract health indicators using the differences in the vectors as degradation progresses. This approach has the advantage of using only normal data to detect subtle performance degradation early and effectively represent health indicators accordingly. [ABSTRACT FROM AUTHOR]

This research unveils an improved indoor positioning method by utilizing inbuilt Inertial Measuring Unit (IMU) and pressure sensor of smartphone and enhancing accuracy in Global Positioning System (GPS)-challenged environments. Initially, Ground Truth (GT) trajectories in 3 cases were obtained by measuring different points with measuring-tape. Each GT trajectory represents actual Positional Data (PD) which is compared with the calculated PD trajectories generated from PD 1, 2, 3, 4, and 5 obtained with 5 different methods. Applying Rotational Transformation (RT), it amalgamates accelerometer and gyroscope data to derive earth linear accelerations (ELA) for PD 1. Applying a Fast Fourier Transform (FFT)-based High Pass Filter (HPF) enhances ELA, refining it into PD 2. Incorporating the magnetic field through the Kalman Filter produces PD 3. Linear Regression (LR) models on IMU and pressure sensor data generate PD 4. Through LR model training with past Positional Data 3, IMU, pressure sensor data, and Positional Data 1, the algorithm achieves superior accuracy in PD 5. Demonstrating a substantial 19.49–78.57% improvement over existing literature data, PD 5 exhibits the least average Root Mean Square Error of 0.51 m. [ABSTRACT FROM AUTHOR]

The combustor is the core component of the scramjet engine, and stable combustion is the prerequisite for the efficient operation of the scramjet engine. This paper investigates combustion stability in a combustor with a thin strut for oxygen supplementation at the trailing edge. LES (Large eddy simulation) was carried out under the combustor inlet conditions of Ma = 2.8, T t = 1680 K, and P t = 1.87 MPa. The results show that as the amount of oxygen supplementation inside the combustor increases, the oscillation forward propagation characteristics of the flame front inside the combustor are formed. First, the combustion instability phenomenon of the flame inside the combustor is analyzed, and then the oscillation frequency of the flame is analyzed by FFT (Fast Fourier Transform) of the flame front and pressure and DMD (Dynamic Mode Decomposition) of the velocity field within one cycle. Finally, the flame forward propagation mechanism inside the combustor is analyzed by pressure-velocity-heat release distribution. The results show that the injection of excess oxygen inside the combustor forms a strong coupling phenomenon of flow and combustion, and the premixed gas downstream of the trailing edge of the strut forms a state of slow combustion to explosion, forming a flame propagation forward and periodic oscillation phenomenon. • LES method is used to capture the flame oscillation behavior with different oxygen supplementation. • The multi-frequency characteristics of combustion oscillations are obtained by using spectrum analysis. • Different combustion modes in flame oscillation and stable regions are discussed. • The mechanism of flame oscillation and propagation upstream is discussed. [ABSTRACT FROM AUTHOR]

The rigid guide is a crucial component of the mine hoisting system, which plays a role in guiding the smooth operation of the hoisting container in the process of mine hoisting. To address the issue of detection devices mounted on mobile equipment affecting normal production, this paper proposes to install the device inside the groove of the rigid guide, and directly collect the vibration signal of the rigid guide while the mine hoisting system is in operation. The collected vibration signals are preprocessed and subjected to fast Fourier transform. To fully extract the fault information hidden in the spectrogram, the vibration signals are transformed into a two-dimensional spectrogram in polar coordinates and used as a sample dataset for training a convolutional neural network (CNN) to achieve fault classification and identification of the rigid guide. Experimental studies on this method show that the accuracy of CNN in identifying rigid guide fault categories reaches 92.63%. Compared to the method of collecting vibration signals from mobile devices, the fault identification accuracy also exceeds 90%. By analyzing the vibration signals of the rigid guide, it is possible to determine whether there is a fault. [ABSTRACT FROM AUTHOR]

Purpose: To reduce the ringing artifacts of the motion‐resolved images in free‐breathing dynamic pulmonary MRI. Methods: A golden‐step based interleaving (GSI) technique was proposed to reduce ringing artifacts induced by diaphragm drifting. The pulmonary MRI data were acquired using a superior–inferior navigated 3D radial UTE sequence in an interleaved manner during free breathing. Successive interleaves were acquired in an incoherent fashion along the polar direction. Four‐dimensional images were reconstructed from the motion‐resolved k‐space data obtained by retrospectively binning. The reconstruction algorithms included standard nonuniform fast Fourier transform (NUFFT), Voronoi‐density‐compensated NUFFT, extra‐dimensional UTE, and motion‐state weighted motion‐compensation reconstruction. The proposed interleaving technique was compared with a conventional sequential interleaving (SeqI) technique on a phantom and eight subjects. Results: The quantified ringing artifacts level in the motion‐resolved image is positively correlated with the quantified nonuniformity level of the corresponding k‐space. The nonuniformity levels of the end‐expiratory and end‐inspiratory k‐space binned from GSI data (0.34 ± 0.07, 0.33 ± 0.05) are significantly lower with statistical significance (p < 0.05) than that binned from SeqI data (0.44 ± 0.11, 0.42 ± 0.12). Ringing artifacts are substantially reduced in the dynamic images of eight subjects acquired using the proposed technique in comparison with that acquired using the conventional SeqI technique. Conclusion: Ringing artifacts in the motion‐resolved images induced by diaphragm drifting can be reduced using the proposed GSI technique for free‐breathing dynamic pulmonary MRI. This technique has the potential to reduce ringing artifacts in free‐breathing liver and kidney MRI based on full‐echo interleaved 3D radial acquisition. [ABSTRACT FROM AUTHOR]

The blades of an axial fan are optimized using artificial neural networks and genetic algorithms in this paper. In first, a parametric axial fan blade model is established with constraints imposed on several parameters. The chord length, maximum camber, maximum camber position, blade thickness, and airfoil stagger angle are considered as an optimization parameter of axial fan. The static pressure efficiency and static pressure of axial fan are regarded as the optimization objectives. An optimization calculation of an axial fan blade is carried out based on the combination of artificial neural network and genetic algorithm. The objective aim of optimization is to improve the static pressure efficiency, the static pressure of axial fan and to reduce the flow loss of axial fan. Numerical results of axial fan demonstrate that the pressure distribution gradient and turbulent kinetic energy contour maps of the optimized axial fan are effectively suppressed within the impeller region compared with that of original axial fan. Furthermore, the internal flow stability of the optimized axial fan also is significantly improved by studying the pressure fluctuation and the Fast Fourier Transform (FFT) of pressure fluctuation. Experimental results of axial fan aerodynamic performance further demonstrate that the static pressure of the optimized axial fan rises as much as 90.93 Pa and the improved static pressure efficiency is effectively improved as much as 7.43% at the design flow rates compared with that of the original axial fan. The application of optimized axial flow fans is of great significance in energy-saving of energy equipment. [ABSTRACT FROM AUTHOR]

Mexico is one of the main countries in the production and exportation of bell pepper (Capsicum annuum) hence, emerging techniques to monitor and assess quality parameters postharvest is a constant topic of interest. On the other hand, color image processing is a non-invasive technique that is gaining popularity in the agro-industrial sector. Color analysis helps as an indirect or correlation measure to quantify and qualify visual quality attributes (size, shape, maturity, etc.) and even non-visual ones such as flavor. Recent techniques involve a pre-processing of the information (changing the RGB space into another) in order to obtain the chromaticity and luminescence parameters that point to the characterization of the physicochemical profile; this stage may present a loss of information. Therefore, this work presents the use of Quaternion Algebra for the management of information as a single entity, avoiding the pre-processing stage. The image is transformed into a pure quaternion; processed using the Fast Fourier Transform described in Quaternions. Image acquisition is carried out using any mobile device or low-cost sensor, which provides an advantage over other proposals. The results show that the determination of physical measurements such as length, width and thickness are characterized according to pre-established standards. In addition, we identify the state of maturity; as well as areas in which damage or injuries are found. The validation of the results obtained was done in consideration of Good Management Practices as well as, the state of the art having a high agreement with them. [ABSTRACT FROM AUTHOR]

The acoustic complexity index (ACI) is a commonly used metric in ecoacoustics, demonstrating reliability across diverse environments and ecological conditions. However, this index requires specific procedures to be applied correctly. Based on the Canberra metric, the ACI is an unsupervised metric formulated to extract information from fast Fourier transform (FFT) sonic matrices. The ACI measures contiguous differences in acoustic energy of each frequency bin along temporal steps (ACItf) and a temporal interval along the frequency bins (ACIft). Aggregating data after an FFT with a clumping procedure allows for better scaling of sonic signals before computing the ACI. A filter must be applied to reduce the effects of nonenvironmental signals produced by microphone electrical noise . Due to the singularity of the index for values of 0, ACI requires ad hoc procedures to exclude element pairs for which one of the elements is equal to 0 from the comparisons. The spectral and temporal sonic signatures are vectors obtained from the sequence of ACItf and ACIft values, respectively. The comparison between sonic signatures using the chord distance index returns spectral and temporal sonic dissimilarities, allowing the evaluation of sonic patterns at different temporal and spatial resolutions. Sonic variability, sonic evenness, and the effective number of frequency bins are further derivative metrics that help interpret sonic heterogeneity by distinguishing the temporal and spatial heterogeneity of sonoscapes. Moreover, this paper proposes changing the terminology of ‘acoustic complexity index' to ‘sonic heterogeneity index.' [ABSTRACT FROM AUTHOR]

Collision cross section (CCS) of organic compounds can be measured via Fourier transform‐based mass spectrometry (MS) by modeling the decay rate of transient signals in the analyzer. Deriving CCS values of low‐mass molecules (mass < 2000 Da and CCS < 500 Å2) with Orbitrap MS is challenging due to their high axial frequencies and small absolute variances in cross‐sectional profiles. Here, we acquired mass spectra of progressively more complex low‐mass analytes using commercial Orbitrap mass spectrometers. The transient signals were processed using Fast Fourier transform (FFT) and short‐time Fourier transform (StFFT) to derive decay constants of multiple select ionic species from a single MS full‐scan experiment. Decay constants were translated into CCS values using at least two internal standards in the same mass spectrum. Our results suggest target ionic species should have high S/N in order to derive CCS values with ≤0.5% uncertainty. Limitations in the precision of CCS measurements reflect local space charge effects that disturb ion motion in the analyzer. The derived CCS values of polymer like fragments of Ultramark 1621 and small molecules such as individual protonated amino acids can achieve average ±1% error with selection of internal standards across a wide mass range. Future studies need to optimize the strategy to select internal standards in order to improve the precision and accuracy of CCS measurements for small molecules via Orbitrap MS. [ABSTRACT FROM AUTHOR]

The accuracy of ZPW-2000 frequency shift signal demodulation is related to the safety and efficiency of high-speed trains. This paper proposed a kind of ZPW-2000 frequency-shift signal detection algorithm based on improved Relaxation algorithm (ZFSD-IR) to address the issue of low accuracy in detecting ZPW-2000 frequency shift signal under strong noise interference, as the traditional fast Fourier transform (FFT) spectrum analysis methods are susceptible to fence effects and spectrum leakage effects. Firstly, the principle of ZPW-2000 jointless track circuit was analyzed, and then the ideal and simplified models of ZPW-2000 frequency shift signal were established, respectively. Finally, the simulation experiments were conducted on ZPW-2000 frequency shift signal detection under different sampling durations and signal-to-noise ratios (SNR). The effectiveness of the proposed algorithm was verified through comparative analysis with the traditional algorithms. The research results indicated that the ZFSD-IR algorithm has better accuracy in carrier-frequency and low-frequency estimation than traditional algorithms, and can achieve accurate detection of ZPW-2000 frequency shift signal under the lower SNR conditions. It has high detection accuracy and good anti-interference ability, ensuring the safe operation of high-speed trains. [ABSTRACT FROM AUTHOR]

The mechanical interactions among integrated cellular structures in soft tissues dictate the mechanical behaviors and morphogenetic deformations observed in living organisms. However, replicating these multifaceted attributes in synthetic soft materials remains a challenge. In this work, we develop a smart hydrogel system featuring engineered stiff cellular patterns that induce strain-driven heterogeneous subdomains within the hydrogel film. These subdomains arise from the distinct mechanical responses of the pattern and film domains under applied mechanical forces. Unlike previous studies that incorporate reinforced inclusions into soft matrices to tailor material properties, our method manipulates the localization, integration, and interaction of these subdomain building blocks within the soft film. This enables extensive tuning of both local and global behaviors. Notably, we introduce a subdomain-interface mechanism that allows for the concurrent customization and decoupling of mechanical properties and shape transformations within a single material system—an achievement rarely accomplished with current synthetic soft materials. Additionally, our use of in-situ imaging characterizations, including full-field strain mapping via digital imaging correlation and reciprocal-space patterns through fast Fourier transform analysis of real-space pattern domains, provides rapid real-time monitoring tools to uncover the underlying principles governing tailored multiscale heterogeneities and intricate behaviors. Replicating the cellular structures in soft tissue has promise in the design of soft materials. Here, the authors report a hydrogel system with cellular patterns in which strain-driven heterogeneous subdomains are induced, allowing local and global behaviour to be tuned. [ABSTRACT FROM AUTHOR]

Bridges will inevitably experience structural damage during long-term operation, which will decrease their structural load bearing capacity and durability. The vibration characteristics of bridge structures can be used as evaluation indicators for accurately diagnosing their deterioration. This study aimed at developing a vibration testing method for identifying bridge deck damage that does not affect traffic and is easy to implement. The vibration tests on the in-service bridge revealed it was mainly subjected to vertical vibrations, with the accelerations in the vertical direction about twice those in the horizontal direction. Based on fast Fourier transform analysis, the dominant vertical vibration frequencies were 2.34 Hz, 4.10 Hz, 10.30 Hz, 14.45 Hz and 18.75 Hz. A series of 0.1–0.3 Hz bandpass filters were used with those frequencies at the centers for modal analysis. The experimental results were then compared to the finite element analysis results. This study proposed a vibration-based damage identification method based on the differences between the bridge deck and both main steel girders. The study confirmed the convenience and effectiveness of using underneath maintenance walkways for wireless vibration testing. [ABSTRACT FROM AUTHOR]

Amperometry is a commonly used electrochemical method for studying the process of exocytosis in real-time. Given the high precision of recording that amperometry procedures offer, the volume of data generated can span over several hundreds of megabytes to a few gigabytes and therefore necessitates systematic and reproducible methods for analysis. Though the spike characteristics of amperometry traces in the time domain hold information about the dynamics of exocytosis, these biochemical signals are, more often than not, characterized by time-varying signal properties. Such signals with time-variant properties may occur at different frequencies and therefore analyzing them in the frequency domain may provide statistical validation for observations already established in the time domain. This necessitates the use of time-variant, frequency-selective signal processing methods as well, which can adeptly quantify the dominant or mean frequencies in the signal. The Fast Fourier Transform (FFT) is a well-established computational tool that is commonly used to find the frequency components of a signal buried in noise. In this work, we outline a method for spike-based frequency analysis of amperometry traces using FFT that also provides statistical validation of observations on spike characteristics in the time domain. We demonstrate the method by utilizing simulated signals and by subsequently testing it on diverse amperometry datasets generated from different experiments with various chemical stimulations. To our knowledge, this is the first fully automated open-source tool available dedicated to the analysis of spikes extracted from amperometry signals in the frequency domain. [ABSTRACT FROM AUTHOR]

Single‐nitrogen‐vacancy (NV) centers in diamond are highly promising quantum nuclear magnetic resonance (NMR) sensors. However, their exposure to many sources of noise, such as surface impurities, shot noise from avalanche photodiode overlaps, and spin‐state projection errors inherent in quantum systems, limits their usefulness. Often, long measurement durations are required to accumulate sufficient data for NMR signal detection via fast Fourier transform spectrometry. For practical reasons, methods to shorten the necessary accumulation time for NMR signal detection are greatly desired. In this article, an on‐line formulation of maximum likelihood estimation (MLE) signal processing for quantum heterodyne NMR measurements is presented as a step toward this goal. This MLE method reduces the required data accumulation time by orders of magnitude and provides good estimates of target frequency locales in real time. These results are significant to practitioners of NMR detection with single‐NV centers in diamond who require a quick litmus test for potential signals when probing a wide area. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper proposed an approach to estimate disc cutter wear utilizing a combination of multiple operational parameters and vibration data collected during shield tunneling operations. The incorporation of vibration signals, notably those originating from acceleration sensors mounted on the back plate of the soil chamber, has markedly enhanced the accuracy of the model. Time‐frequency domain features were extracted through analysis methods such as Fast Fourier Transform (FFT), Short‐Time Fourier Transform (STFT), and Continuous Wavelet Transform (CWT). A predictive model utilizing vibration and shield operation parameters was developed using the XGBoost algorithm, and a deep GoogLeNet Convolutional Neural Network (CNN) was trained on time‐frequency graphs from the CWT. In addition, this study also investigated the impact of signal duration on wavelet image information and model accuracy. In the Huang‐Shang Intercity Railway Project, the approach effectively assessed disc cutter wear during tunneling operations and dynamically optimized the operational parameters of the shield tunnel machine through predictive analysis. [ABSTRACT FROM AUTHOR]

Background: The autonomic nervous system (ANS) is affected by several factors, including major nutrients. However, their effects on the ANS remains unclear. Most studies had several limitations. They focused on humans, therefore they had difficulties excluding factors other than the nutrients. Their observation periods were too short (<4 hours) to align with typical absorption times of carbohydrates versus fats. They assessed the effects compared with the pre-prandial state rather than comparisons between different nutrient types. Objective: We aimed to investigate the effects of carbohydrates and fats on the ANS. Method: We employed a rat model to exclude the effects of external stimuli, used sufficient observation period, and compared the ANS parameters among animals fed 3 different diets. The rats were divided into carbohydrate-rich-diet (carb group) and fat-rich-diet (fat group) groups. We investigated the effects of carbohydrates and fats on the ANS by measuring heart rate variability parameters in rats. Electrodes and electrocardiography (ECG) transmitters were implanted in 14 Wistar rats maintained on a standard diet for 2 days followed by the experimental diets for 2 days. ECG readings were continuously recorded for 4 days. The R waves, function of the R-R interval, and time were calculated. A fast Fourier transform was used to obtain the power spectrum of the fluctuation (low frequency [LF: 0.2-0.75 Hz]; high frequency [HF: 0.75-3.0 Hz]; LF/HF ratio). Result: Compared with the standard-diet group, the carb group showed significantly increased HF activity, while the fat group showed a significantly increased LF/HF ratio. Conclusion: The results reveal a link between macronutrients and ANS activity. [ABSTRACT FROM AUTHOR]

The selection of a target when training deep neural networks for speech enhancement is an important consideration. Different masks have been shown to exhibit different performance characteristics depending on the application and the conditions. This paper presents a comprehensive comparison of several different masks for noise reduction in cochlear implants. The study incorporated three well-known masks, namely the Ideal Binary Mask (IBM), Ideal Ratio Mask (IRM) and the Fast Fourier Transform Mask (FFTM), as well as two newly proposed masks, based on existing masks, called the Quantized Mask (QM) and the Phase-Sensitive plus Ideal Ratio Mask (PSM+). These five masks are used to train networks to estimate masks for the purpose of separating speech from noisy mixtures. A vocoder was used to simulate the behavior of a cochlear implant. Short-time Objective Intelligibility (STOI) and Perceptual Evaluation of Speech Quality (PESQ) scores indicate that the two new masks proposed in this study (QM and PSM+) perform best for normal speech intelligibility and quality in the presence of stationary and non-stationary noise over a range of signal-to-noise ratios (SNRs). The Normalized Covariance Measure (NCM) and similarity scores indicate that they also perform best for speech intelligibility/gauging the similarity of vocoded speech. The Quantized Mask performs better than the Ideal Binary Mask due to its better resolution as it approximates the Wiener Gain Function. The PSM+ performs better than the three existing benchmark masks (IBM, IRM, and FFTM) as it incorporates both magnitude and phase information. [ABSTRACT FROM AUTHOR]

We propose a new method for predicting the solar cycle in terms of the sunspot number (S N) based on multivariate machine learning algorithms, various proxies of solar activity, and the spectral analysis of all considered time series via the fast Fourier transform (through the latter we identify periodicities with which to lag these series and thus generate new attributes –predictors– for incorporation in the prediction model). This combination of three different techniques in a single method is expected to enhance the accuracy and reliability of the solar activity prediction models developed to date. Thus, predictive results for S N are presented for Solar Cycles 25 (the current one) and 26 (using the 13-month smoothed S N , version 2) up until January 2038, yielding maximum values of 134.2 (in June 2024) and 115.4 (in May 2034), respectively, with a root mean squared error (RMSE) of 9.8. These results imply, on the one hand, a maximum of Cycle 25 below the average and, on the other hand, a lower peak than the preceding ones for Cycle 26, suggesting that Solar Cycles 24, 25, and 26 are part of a minimum of the centennial Gleissberg cycle, as occurred with Cycles 12, 13, and 14 in the final years of the 19th century and the early 20th century. [ABSTRACT FROM AUTHOR]

Filling of oxygen vacancies in manganese-oxide nanosheets with Pt single-atoms or nanoparticles yields nanozymes with high catalytic activity. Catalytic activity depended on the diameter of the Pt nanoparticles used for loading the nanosheet. Pt nanoparticles with intermediate diameters (3.0 nm), possessing well-developed (1 1 1) crystal planes, yielded equal catalytic activities as Pt single-atoms. [Display omitted] • Manganese-oxide nanosheets loaded with Pt single-atoms or Pt nanoparticles are compared. • Sheets with Pt single-atoms or 3.0 nm diameter Pt nanoparticles have high catalytic activities. • 1.5 nm Pt nanoparticles do not possess well-developed, highly reactive (1 1 1) crystal planes. • 7 nm Pt nanoparticles coalesce on a nanosheet, changing the Mn oxidation state. • Under-sized (1.5 nm) and over-sized Pt (7.0 nm) nanoparticles yield lower catalytic activity. Hybrid-nanozymes are promising in various applications, but comprehensive comparison of hybrid-nanozymes composed of single-atoms or nanoparticles on the same support has never been made. Here, manganese-oxide nanosheets were loaded with Pt-single-atoms or differently-sized nanoparticles and their oxidase- and-peroxidase activities compared. High-resolution Transmission-Electron-Microscopy and corresponding Fast Fourier Transform imaging showed that Pt-nanoparticles (1.5 nm diameter) had no clear (1 1 1) crystal-planes, while larger nanoparticles had clear (1 1 1) crystal-planes. X-ray Photo-electron Spectroscopy demonstrated that unloaded nanosheets were composed of MnO 2 with a high number of oxygen vacancies (V o /Mn 0.4). Loading with 7.0 nm Pt-nanoparticles induced a change to Mn 2 O 3 , while loading with 1.5 nm nanoparticles increased the number of vacancies (V o /Mn 1.2). Nanosheets loaded with 3.0 nm Pt-nanoparticles possessed similarly high catalytic activities as Pt-single-atoms. However, loading with 1.5 nm or 7.0 nm Pt-nanoparticles yielded lower catalytic activities. A model is proposed explaining the low catalytic activity of under- and over-sized Pt-nanoparticles as compared with intermediately-sized (3.0 nm) Pt-nanoparticles and single-atoms. Herewith, catalytic activities of hybrid-nanozymes composed of single-atoms and intermediately-sized nanoparticles are put a par , as confirmed here with respect to bacterial biofilm eradication. This conclusion facilitates a balanced choice between using Pt-single-atoms or nanoparticles in further development and application of hybrid-nanozymes. [ABSTRACT FROM AUTHOR]

The study of the temporal evolution of chloride ions in groundwater is important for identifying whether their sources are due to anthropogenic pollution or natural factors. Groundwater in the northern part of Dalat Banner, Ordos City, has high chloride ion content and exhibits strong temporal variability. To identify the source of chloride ions and reveal their evolution mechanisms, the fast Fourier transform (FFT) was used to determine the trend and cycle of chloride ion evolution, and the groundwater dynamics field combined with multivariate statistical analysis was used to identify the source of chloride ion pollution. Calculations show that the background value of chloride ions in groundwater in the study area is 195.17 mg/L, reaching a maximum of 459 mg/L under the influence of rainfall. The fluctuation of chloride ion concentration is mainly related to the total rainfall in the study area over 165 days, and a single rainfall of more than 15 mm affects the concentration of chloride ions in groundwater. The results of this study show that the background values of chloride ions are mainly influenced by the groundwater dynamical field, and the temporal volatility is mainly influenced by atmospheric rainfall. [ABSTRACT FROM AUTHOR]

To study mechanisms of jet atomization, a novel method of experimentation utilizing the resonation of diaphragms made from thin steel plates has been previously developed. In the experiments, a diaphragm covered by a film of water emitted acoustic sounds, and jet atomization from the water film was observed. Experiments using diaphragms composed of different materials and fast Fourier transformation analysis of the acoustic sound revealed that jet atomization occurred under limited surface conditions of the diaphragm and a specific range of frequency. In this article, the dynamics of a resonating body composed of the diaphragm and water film were analyzed using the finite element method with a combination of theoretical analyses of surface waves of water, such as the well-known Lang's equation. The present FEA results, from harmonic response analyses with consideration of viscous damping effect due to interaction between the diaphragm and water film, precisely confirmed the results of FFT analysis previously obtained by the experiment. Specifically, the peak frequency of the frequency response agreed well with the FFT results, and the shift of the peak frequency and attenuation due to the interaction in the analyses corresponded with the difference in surface conditions between the hydrophilic and hydrophobic materials of the diaphragm in the experiments. Our interpretation of the mechanism of jet atomization is expanded by the present numerical results. [ABSTRACT FROM AUTHOR]

In power transformers, lightning strikes, switching operations, and short circuit faults can deform the winding insulation, resulting in an electric arc between the windings. If the arc is not detected in its initial phase, it may lead to a solid short circuit and damage the transformer, potentially causing an explosion due to overheating and high pressure. In this study, winding arcs in the transformer are identified from the terminal current and voltage signals. A 3D magnetic model of a 15 MVA power transformer is constructed in Ansys@Maxwell, and the nonlinear arc model is simulated in Matlab@Simulink. The transient voltage and current signals related to the arcing conditions at five different points in the high-voltage side winding are obtained by running Ansys and Matlab simultaneously using ANSYS@Simplorer (Twin Builder). These signals are transformed into the frequency domain using Fast Fourier Transform (FFT). The arcs are detected from the transient-generated frequency components of the transformer voltage and current signals. [ABSTRACT FROM AUTHOR]

Spectrum sensing (SS) technology is essential for cognitive radio (CR) networks to effectively identify and utilize idle spectrum resources. Due to the influence of noise characteristics in the channel, providing accurate sensing results is challenging. In order to improve the performance of SS under non-Gaussian noise and overcome the limitations of existing methods that are mostly based on a single feature, we propose a novel time-frequency cross fusion network (TFCFN). Specifically, we utilize gated recurrent units (GRU) to capture long-term dependencies in the time domain on the original signals, meanwhile, we perform a fast Fourier transform (FFT) on the original signals to obtain the frequency domain information, and subsequently use convolutional neural networks (CNN) to extract the local spatial features in the frequency domain. Ultimately, these time-domain and frequency-domain features are dynamically fused through a cross-attention mechanism to construct more comprehensive and robust features for signal classification. We use generalized Gaussian distribution (GGD) as the noise model and reconstruct the RadioML2016.10a dataset to explore the performance under various noise conditions. The experimental results show that compared with the baseline methods, TFCFN exhibits better detection ability and maintains lower complexity in both Gaussian and non-Gaussian noise environments. Notably, when the shape parameter of GGD is set to 0.5 and the signal-to-noise ratio (SNR) of the received signal is -16dB, it can maintain the probability of false alarm ( P f ) of 10% while still ensuring the probability of detection ( P d ) of over 90%. [ABSTRACT FROM AUTHOR]

The bundengan is a unique traditional musical instrument with plucked strings from Wonosobo, Central Java, Indonesia. It can produce sounds that imitate the gong (i.e., a part of the gamelan instrument). The bundengan was initially constructed by duck herders as a means of shielding themselves from inclement weather while caring for their flocks. They also engage in musical activities and singing. The distinctive sound of the bundengan is created by plucking a set of strings fitted with tiny bamboo clips using the right hand and three elongated, slender bamboo blades with the left hand. The sound effect is produced by the bandulan, a small piece of bamboo attached to the string. The tuning of the bundengan depends on the player's instinct. This study analyzed the pitch and timbre of the bundengan strings. Using Fast Fourier Transform (FFT), the sound from a plucked string yields the frequency spectrum of the actual vibrations from the strings. The results were used to validate the frequency as heard. The results showed that the pitch from the measured frequencies is not similar to the pitch as heard. The bundengan is tuned to a pitch corresponding to the timbre rather than a specific pitch. [ABSTRACT FROM AUTHOR]

One of the most critical challenges in optical fiber communication systems using non-orthogonal multiple access (NOMA) techniques is reducing the peak-to-average power ratio (PAPR). Optical NOMA often leads to high PAPR, causing non-linear distortion and limiting the system's gain. The advanced Selected Mapping (SLM) algorithm is introduced in this work to reduce the PAPR of the O-NOMA. In conventional SLM, multiple phase sequences are applied to the input data, which requires numerous Inverse Fast Fourier Transform, increasing computational complexity. The proposed approach simplifies this by selecting a central reference phase sequence, reducing the number of candidate sequences and the number of IFFT operations required. This reduction in complexity does not significantly compromise the PAPR reduction capability, making the method more efficient. The SLM algorithm addresses this issue by applying the phase rotation factor to the transmitted signals, redistributing the power, and reducing the peak levels. This article highlights the benefits and challenges of implementing SLM in optical NOMA systems, including its compatibility with existing NOMA schemes. Matlab 2016 analyzes parameters such as bit error rate (BER), PAPR, and out-of-band radiation. A 66% PAPR reduction can be obtained using the suggested SLM with various phase variables. Furthermore, there is a −2650 reduction in spectrum leakage. The simulation outcomes of the work reveal the potential of SLM to reduce PAPR and enhance the BER and PSD performance of optical NOMA compared with the existing algorithms. [ABSTRACT FROM AUTHOR]

Total pressure inlet distortion significantly impairs the aerodynamic performance and flow stability of centrifugal compressors. This paper presents a full annulus unsteady numerical simulation to investigate the flow mechanisms under varying inlet distortion parameters. First, the flow characteristics of a uniform inflow is studied as a baseline for comparison with distorted inlet conditions. Results indicate that the interaction between shock waves and tip leakage flow is a primary factor leading to aerodynamic instability. The instability signal in this region is detected through fast Fourier transform and frequency slice wavelet transform (FSWT). Under near stall condition, different distortion parameters are set to research the effects on aerodynamic performance, flow mechanisms, and instability characteristics. The results reveal that distortion intensity has the most significant impact, causing a maximum stall margin loss of 55.43%, followed by distortion angle with a maximum stall margin loss of 43.02%. Total pressure inlet distortion leads to a deterioration in aerodynamic performance, primarily due to premature occurrences of unstable flow phenomena such as leading-edge spillage and trailing-edge backflow. The onset key features triggering aerodynamic instability are identified as leading-edge spillage vortex, tip leakage vortex, and passage vortex. The continuous disintegration of the tip leakage vortex results in low-frequency fluctuating energy exhibiting multipeak characteristics, with pulsation peaks centered around 0.5 BPF, related to the spike stall of the compressor. The high-energy frequency band dissipates over time in the time–frequency spectrum, as shown by FSWT results, indicating the characteristics of instability in the flow. [ABSTRACT FROM AUTHOR]

The pump converts mechanical energy into potential energy, and a mixed-flow-pump combines the characteristics of an axial flow pump and a centrifugal pump. When the mixed-flow-pump operates at low flow conditions, performance instability in the hump region appears on the performance curve. This study investigates the underwater acoustic signal in this area through experiments, computational fluid dynamics (CFD), and computational aero acoustics (CAA). The hysteresis factor calculated by cross correlation (CC) is utilized to improve the dynamic time warping (DTW) signal comparison verification method. The improved method (CC-DTW) improves the ability of DTW in signal comparison and verification. Compared with the fast Fourier transform (FFT) method, it is more convincing in the comparison of experimental and simulation pressure pulsation signals. After verifying the effectiveness of pressure pulsation signals and underwater acoustic signals, a combined empirical wavelet transform and FFT method is used to analyze underwater acoustic signal in instability regions. The results indicate that the depth of instability aligns with the FFT frequence ratio of the intrinsic mode function. Based on the feature, a criterion for determining instability states in mixed-flow-pumps is proposed. [ABSTRACT FROM AUTHOR]

To stabilize the operation of pumped storage power station, an orthogonal experimental design was proposed to optimize the return channel with the splitter blades of the pump turbine. The calculation results of various return channel models under multiple operating conditions indicated that inlet distance a1 and blade number z considerably affect the efficiency and flow pattern in the flow passage. The optimal scheme improved efficiency by nearly 3% at 0.6Qd. The Savitzky–Golay filtering method and Fast Fourier Transform were used to analyze the unsteady characteristics of the pump turbine in pump model. The amplitude of pressure pulsations at the blade passing frequency in the vaneless space and the interference zone between the guide vane and return channel reduced considerably, and the pressure pulsation amplitude in the vaneless zone decreased by 50%, 48%, and 20% for 0.6Qd, 1.0Qd, and 1.1Qd operating conditions. A Continuous Wavelet Transform was used to analyze frequency signals during the shutdown transition process. The optimization of the splitter blades improved the flow pattern in their corresponding flow passages and suppressed high-amplitude pressure pulsations in the unit for the stable operation of the pumped storage power station. [ABSTRACT FROM AUTHOR]

The frequency characteristics of cavitation fluctuations in high-speed cavitating jets are intricate due to the coupling mechanisms of unsteady behaviors. This study employs high-speed photography to experimentally investigate the relationship between frequency characteristics and the unsteady behavior of cavitating jets with various cavitation numbers. Temporal evolution patterns of the cavitating jets are analyzed through spatiotemporal (s-t) diagrams. The spatial distribution and temporal evolution of cavitation fluctuation frequencies are examined using fast Fourier transform (FFT) and continuous wavelet transform (CWT), respectively. Proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD) are employed to identify coherent structures and their corresponding frequencies. In results, the s-t diagrams reveal the distinct regions influenced by cavitation shedding and collapse. FFT results indicate that upstream of the jet trajectory, spectral energy is concentrated in the shedding band, shifting toward lower frequencies with increasing axial distance. The CWT spectrum exhibits a single peak in the upstream, identifying it as the shedding frequency. POD modes associated with shedding dominate the energy contribution at higher cavitation numbers, while they become less prominent at lower cavitation numbers. DMD extracts and identifies coherent structures associated with shedding through frequency-specific decomposition. [ABSTRACT FROM AUTHOR]

Parametric array loudspeakers have been widely used in audio applications for generating directional audio beams. However, accurately calculating audio sound with a low computational load remains challenging, even for basic axisymmetric source profiles. This work addresses this challenge by extending the King integral in linear acoustics to incorporate both cumulative and local nonlinear effects, under the framework of the quasilinear solution without the paraxial approximation. The proposed method exploits the azimuthal symmetry in cylindrical coordinates to simplify modeling. To further improve computational efficacy, fast Hankel and Fourier transforms are employed for the radial and beam radiation directions, respectively. Numerical results with both uniform and focusing profiles demonstrate the advantages of the proposed approach over the traditional spherical wave expansion and direct integration methods, especially for larger aperture sizes. Specifically, for typical configurations with source aperture size of 0.2 m, we observe at least a 24-fold improvement in computational speed and a 227-fold reduction in memory requirements. These advancements allow us, for the first time, to present the sound field radiated by parametric array loudspeakers with a large aperture size of up to 0.5 m, without paraxial approximations. The implementation codes are available on https://github.com/ShaoZhe-LI/PAL_King. [ABSTRACT FROM AUTHOR]

We used numerical methods to define the normative structure of the different stages of sleep and wake (W) in a pilot study of 19 participants without pathology (18–64 years old) using a double-banana bipolar montage. Artefact-free 120–240 s epoch lengths were visually identified and divided into 1 s windows with a 10% overlap. Differential channels were grouped into frontal, parieto-occipital, and temporal lobes. For every channel, the power spectrum (PS) was calculated via fast Fourier transform and used to compute the areas for the delta (0–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), and beta (13–30 Hz) bands, which were log-transformed. Furthermore, Pearson's correlation coefficient and coherence by bands were computed. Differences in logPS and synchronization from the whole scalp were observed between the sexes for specific stages. However, these differences vanished when specific lobes were considered. Considering the location and stages, the logPS and synchronization vary highly and specifically in a complex manner. Furthermore, the average spectra for every channel and stage were very well defined, with phase-specific features (e.g., the sigma band during N2 and N3, or the occipital alpha component during wakefulness), although the slow alpha component (8.0–8.5 Hz) persisted during NREM and REM sleep. The average spectra were symmetric between hemispheres. The properties of K-complexes and the sigma band (mainly due to sleep spindles—SSs) were deeply analyzed during the NREM N2 stage. The properties of the sigma band are directly related to the density of SSs. The average frequency of SSs in the frontal lobe was lower than that in the occipital lobe. In approximately 30% of the participants, SSs showed bimodal components in the anterior regions. qEEG can be easily and reliably used to study sleep in healthy participants and patients. [ABSTRACT FROM AUTHOR]

In this contribution, the time-domain harmonic state estimation is evaluated using the real-time digital simulation, Kalman filter, an optimal measurement algorithm and a physical scaled-down laboratory implementation. This methodology is implemented using MATLAB/Simulink® and runs on RT-LAB® platform in real time. The optimal placement algorithm of measurement devices is used to obtain full observability of the system. This measurement algorithm finds the optimal placement with a limited number of measurement devices, whose measurements may be contaminated with noise. The measurement model is implemented in the physical test power system. Besides, the state estimator assesses the system inputs (internal generator voltages, nonlinear electrical load variables) whose dynamics are totally unknown. The harmonic content of the waveforms is obtained through the fast Fourier transform. The estimated responses in the case studies using the RT-LAB® platform are validated through direct comparison against those obtained in the physical implementation of the electrical power system, obtaining satisfactory results. [ABSTRACT FROM AUTHOR]

In this paper, we propose a non-convex model with fractional-order applied to image deblurring problems. In the new model, fractional-order gradients have been introduced to preserve detailed features, and a source term with a blurry kernel is used for deblurring. This aspect of the model ensures that it can handle various blurring scenarios. Additionally, we devise an algorithm that maintains the non-expansiveness of the support set for image gradients, serving as a critical component in our approach to address image deblurring issues. After approximate linearization, the algorithm can be easily implemented. Some standard image processing techniques similar to fast Fourier transform can be utilized. Global convergence has likewise been confirmed and established. Moreover, we have also demonstrated that the proposed deblurring algorithm exhibits edge preservation properties. Compared with several existing classic models, the proposed method maintains a good balance between detail preservation, edge preservation, and deblurring. In addition, compared with several classic methods, the proposed method improved PSNR and SSIM by 0.9733 and 0.0111, respectively. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to assess the optical quality of myopic and presbyopic IPCLs with different additional powers, and to investigate the effects of pupil size on the optical quality of these IPCLs using an in-vitro modulation transfer function (MTF) measurement system. Methods: Linear scatter functions (LSFs) were recorded using the OPAL Vector system and an eye phantom consisting of wet cells filled with a balanced salt solution. A myopic IPCL or a presbyopic IPCL was placed in the posterior chamber of this model. The MTF was calculated from the LSF using the fast Fourier transform techniques. The effective apertures were set at 2.0 to 5.0 mm in 1.0 mm steps. Results: The in-focus MTF values of the myopic IPCL and presbyopic IPCL with additional powers of + 2.0 and + 4.0 diopters at 100 cycles/mm for an effective aperture of 3.0 mm were 43%, 27%, and 24%, respectively. The in-focus MTF value of both myopic and presbyopic IPCLs was the highest when the effective aperture was set at 3.0 mm, and it gradually worsened when the effective aperture became larger than 3.0 mm at 20, 60, and 100 cycles/mm. Conclusions: Both myopic and presbyopic IPCLs provided excellent MTF values, but the additional power profile can deteriorate optical performance in presbyopic IPCL-implanted eyes, even with a low additional power. Pupil size can influence visual quality in IPCL-implanted eyes for both myopia and presbyopia. Key Messages: What is known: • Implantable phakic contact lens (IPCL) has been reported to be effective for the treatment of both myopia and presbyopia, with a noticeable advantage in reducing the patients' cost burden. • The optical properties of IPCL have not yet been thoroughly investigated, neither for myopic IPCLs nor for presbyopic IPCLs. What is new: • The MTF of presbyopic IPCL was slightly lower than that of myopic IPCL even with a low additional power. • The MTF values of both myopic and presbyopic IPCLs were the highest for a 3.0 mm-pupil size, with gradually worsened for a larger pupil size. [ABSTRACT FROM AUTHOR]

Polydopamine (PDA)-coated Zn–Al-layered double hydroxide (Zn-Al LDH@PDA) have been synthesized through a simple and green procedure. In the present study, for the first time, Pd nanoparticles were successfully deposited using Zn-Al LDH@PDA as an efficient reducing biotemplate and stabilizing support. In this protocol, Pd ions were adsorbed on surfaces of Zn-Al LDH@PDA through immersion of the Zn-Al LDH@PDA nanocomposite into a palladium plating bath. Next, they were reduced in situ to palladium nanoparticles using PDA's N-containing groups and reducing ability. The structure, morphology and physicochemical properties of the synthesized nanoparticles were characterized by different analytical techniques such as energy-dispersive X-ray spectroscopy (EDS), field emission scanning electron microscope (FE-SEM), Fourier-transform infrared spectroscopy (FT-IR) spectroscopy, transmission electron microscopy (TEM), high resolution transmission electron microscopy (HR-TEM), fast Fourier transform (FFT),inductively coupled plasma (ICP), nitrogen physical adsorption (BET), elemental mapping and X-ray Diffraction (XRD).The catalytic potential of Zn-Al LDH@PDA/Pd NPs nanocatalyst was then evaluated in the Suzuki–Miyaura coupling reactions. These tests indicated that Zn-Al LDH@PDA/Pd NPs nanocatalyst is tolerant against various functional groups, and the desired biphenyl products were acquired with good to high yields within short times. The Zn-Al LDH@PDA/Pd NPs nanocatalyst was isolated by centrifuge and it was stable enough to be reused for 7 consecutive times without considerable loss in activity. [ABSTRACT FROM AUTHOR]

During the structural health monitoring of bridges, it has been observed that the vibration data collected can sometimes be randomly lost or sampled non-uniformly. This leads to a low signal-to-noise ratio in the spectral functions of the measured data, making it difficult to identify weak modes. To address this issue, a framework for operational modal identification is proposed in this study. It utilizes the fast Bayesian fast Fourier transform (FFT) method to estimate the modal parameters of highway bridges considering the non-uniform monitoring data. The initial frequency parameters for the fast Bayesian FFT approach are automatically determined using the proposed autoregressive (AR) power spectral density (PSD)-guided peak picking method. This overcomes the challenge of capturing initial frequencies related to weakly contributed modes. Additionally, the bandwidth parameter for each mode is determined using the modal assurance criterion (MAC) of the first left singular vectors of PSD matrices. Furthermore, when analyzing non-uniform vibration data, it is recommended to use the non-uniform FFT (NUFFT) for calculating PSD functions in order to improve identification accuracy. The proposed method is validated using acceleration data from both a numerical model and a real-world bridge. The results demonstrate that the identification uncertainty of modal parameters increases with higher non-uniform levels. [ABSTRACT FROM AUTHOR]

Due to the extensive use of Unmanned Aerial Vehicles (UAVs) and the co-evolution of current technology, a key introduction to fault detection has arisen in recent studies in order to prevent unfortunate consequences. In this study, vibration-based signals from a commercially available innovative quadcopter flying in hover mode are collected using a vibration accelerometer, a data acquisition device, and a laptop. An ADXL335 accelerometer is fixed on the center of the drone where the centerlines of the four blades intersect. The superposition of numerous vibration arrangements over identical spectra hinders the ability to analyze the spectral data in the manner required to locate any framework's discrete vibration. This work presents a technique for separating a synthesized vibration signal towards discrete vibrations and other extraneous vibrations of a structure utilizing the Discrete Wavelet Transform (DWT) integrated with the Fast Fourier Transform (FFT). The research article findings in this study demonstrate the reliability and applicability of specific categories of discrete vibrations that are sorted out during the structural change evaluation to develop the best feasible strategy for removing the undesired and unanticipated vibration components and noise. The methodology demonstrated in this paper has the potential for practical application to multirotor UAVs in general. [ABSTRACT FROM AUTHOR]

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This article presents a study on the vocalizations of Atlantic white-sided dolphins in the North Atlantic Ocean. The dolphins' vocal repertoire includes echolocation clicks, whistles, and burst pulses. The study found that the dolphins' clicks have a median peak frequency of 25 kHz and their whistles exhibit regular frequency changes. The research also suggests that the dolphins may produce burst pulses and buzzes during foraging. These findings contribute to our understanding of the species' vocalizations and can aid in conservation efforts. [Extracted from the article]

Pattern selection is crucial for optimizing the calibration process of optical proximity correction (OPC) models in computational lithography. However, it remains a challenge to achieve a balance between representative coverage and computational efficiency. This work presents a comprehensive evaluation of the feature vectors' (FVs') effectiveness in pattern selection for OPC model calibration, leveraging key performance indicators (KPIs) based on Kullback–Leibler divergence and distance ranking. Through the construction of autoencoder-based FVs and fast Fourier transform (FFT)-based FVs, we compare their efficacy in capturing critical pattern features. Validation experimental results indicate that autoencoder-based FVs, particularly augmented with the lithography domain knowledge, outperform FFT-based counterparts in identifying anomalies and enhancing lithography model performance. These results also underscore the importance of adaptive pattern representation methods in calibrating the OPC model with evolving complexities. [ABSTRACT FROM AUTHOR]

Simultaneous illumination and communication using solid-state lighting devices like white light-emitting diode (LED) light sources is gaining popularity. The white light LED comprises a single-colored yellow phosphor excited by the blue LED chip. Therefore, color-quality determining parameters like color-rendering index (CRI), correlated color temperature (CCT), and CIE 1931 chromaticity coordinates of generic white LED sources are poor. This article presents the development of multi-color phosphors excited by a blue LED to improve light quality and bandwidth. A multi-layer stacking of phosphor layers excited by a blue LED led to the quenching of photoluminescence (PL) and showed limited bandwidth. To solve this problem, a lens-free, electrically powered, broadband white light source is designed by mounting multi-color phosphor LEDs in a co-planar ring-topology. The CRI, CCT, and CIE 1931 chromaticity coordinates of the designed lamp (DL) were found to be 90, 5114 K, and (0.33, 0.33), respectively, which is a good quality lamp for indoor lighting. CRI of DL was found to be 16% better than that of white LED (WL). Assessment of visible light communications (VLC) feasibility using the DL includes time interval error (TIE) of data pattern or jitter analysis, eye diagram, signal-to-noise ratio (SNR), fast Fourier transform (FFT), and power spectral density (PSD). DL transmits binary data stream faster than WL due to a reduction in rise time and total jitter by 31% and 39%, respectively. The autocorrelation function displayed a narrow temporal pulse for DL. The DL is beneficial for providing high-quality illumination indoors while minimizing PL quenching. Additionally, it is suitable for indoor VLC applications. [ABSTRACT FROM AUTHOR]