Composite materials are critical components in advanced equipment such as aerospace, however, delamination defects that readily arise during manufacturing and in service present serious risks to equipment safety. Terahertz non-destructive testing is highly effective for analyzing the internal structure of composite materials, making it an effective approach for precise identification of delamination defects. Current terahertz detection approaches mainly depend on single domain features, making it difficult to capture complementary information from both the time and frequency domains. To address this, a Time-Frequency Feature-fusion Network (TFFN) is proposed. In this network, a three-branch architecture is employed: local transient patterns and pulse-related structural features are extracted by the local time-frequency branch; damage-sensitive frequency bands are focused on by the frequency-domain branch through a channel-space-frequency band attention mechanism; and deep integration of time-frequency features is achieved by the time-frequency fusion branch using Manifold Mixup. Finally, the features extracted from the three branches are adaptively fused via a cross-branch attention mechanism, and defect identification is accomplished by the classifier. Experimental results show that this method achieves accuracies of 98.40% on the glass fiber reinforced polymer (GFRP) dataset and 98.63% on the quartz fiber reinforced polymer (QFRP) dataset, surpassing the best existing method by 2% and 1.25%, respectively. A substantial improvement in both defect identification accuracy and the model's generalization ability for layered structures is thereby achieved.

Precise prediction of CO and O<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> content is crucial for enhancing energy efficiency and reducing pollutant emissions in the ausmelt furnace (AF) tin smelting process. Deep learning prediction methods based on sensor data have been widely used to predict key variables in industrial processes. However, due to the complex smelting process and variable operating conditions in the AF, existing sensor models face challenges in capturing the dynamic coupling of variables and the composite periodic characteristics. To address these issues, we propose time-frequency interaction network (TFiNet) for predicting the trends in CO and O<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> content within the AF. Specifically, the time-domain and frequency-domain analyses module are designed to extract rich time-frequency features. Moreover, a dual-layer Mamba module is introduced in the time domain to extract the dynamic relationships between variables from multiple perspectives. Additionally, to further extract periodic features, we constructed a fourier time/variable transformation in the frequency domain to explore each variable’s frequency characteristics and the inter-variable frequency correlations. Finally, to verify the effectiveness of our proposed method, extensive experiments are performed on a smelting process dataset. The experimental results demonstrate that the proposed method outperforms TimesNet, iTransformer, FEDformer, DLinear and LightTS.

Photonics signal processing has emerged as a promising technique for ultrafast optical and microwave signal processing. Among these, the linear optical wave energy redistribution (OWER) methodology offers unprecedented performance, such as low latency, ultrabroad operation bandwidth, and high energy efficiency. The OWER is based on linear phase manipulation techniques, e.g., the time lens and dispersive line, and can redistribute the energy of the optical waveform along the time domain to achieve user-defined signal processing functionalities. Here, we review recent OWER methods to provide an insight into this processing strategy, including photonic spectrograms for ultrafast optical and microwave signals and time-frequency manipulation for time-varying microwave waveforms. As current demonstrations of OWER systems are based on bulky, fiber-optics devices, the chip-scale implementation is desired to reduce the footprint, cost, and power consumption. Toward this end, we provide here an overview of the state of the art in integrated key photonic or optoelectronic components used in the OWER system, ranging from the most essential passive component, i.e., on-chip dispersive lines, to active devices, such as the modulators, integrated lasers components, and photodetectors.

Near real-time ship monitoring is crucial for ensuring safety and security at sea. Established ship monitoring systems are the automatic identification system (AIS) and marine radars. However, not all ships are committed to carry an AIS transponder and the marine radars suffer from limited visibility. For these reasons, airborne radars can be used as an additional and supportive sensor for ship monitoring, especially on the open sea. State-of-the-art algorithms for ship detection in radar imagery are based on constant false alarm rate (CFAR). Such algorithms are pixel-based and therefore it can be challenging in practice to achieve near real-time detection. This letter presents two object-oriented ship detectors based on the faster region-based convolutional neural network (R-CNN). The first detector operates in time domain and the second detector operates in Doppler domain of airborne Range-Compressed (RC) radar data patches. The Faster R-CNN models are trained on thousands of real X-band airborne RC radar data patches containing several ship signals. The robustness of the proposed object-oriented ship detectors is tested on multiple scenarios, showing high recall performance of the models even in very dense multitarget scenarios in the complex inshore environment of the North Sea.

This project is titled “A Time-Frequency Based Suspicious Activity Detection for Anti-Money Laundering.” The rapid growth of digital financial transactions has attracted millions of users worldwide. However, this expansion has also led to more financial crimes, as malicious actors exploit banking systems to launder money by disguising illegal transactions as legitimate ones. To tackle this problem, there is a strong need to implement an automatic real-time detection mechanism for suspicious transactions in financial systems. In response to these challenges, we proposed a new time-frequency based machine learning framework to detect and classify suspicious financial activities. This framework uses statistical feature extraction methods along with Fast Fourier Transform (FFT) to analyze transaction patterns in both time and frequency domains. We then process these features using a Random Forest classifier, which helps learn effective representations of transaction behavior and make accurate classifications.

Optical encoders play an important role in optical communication. Miniaturized and ultra‐fast designs that push the limits of innovation are urgently needed. This research presents a novel, miniaturized architecture for a 4‐to‐2 all‐optical encoder employed on a 2D photonic crystal platform. The suggested architecture comprises all‐optical OR gates that utilize ring resonators having air holes in a substrate of silicon arranged in a hexagonal geometry working at 1.55 µm resonant wavelength. The linear interference phenomenon serves as the operational basis, leading to minimal power usage. The suggested architecture strives for both scalability and optical integrated circuit compatibility while reducing footprint and efficient coupling. Plane wave expansion technique has been used to obtain the photonic band gap, while the relevant performance metrics are calculated and analyzed using the finite difference time domain method. Both the hole radius ( r ) and the lattice constant (a) in the suggested design have been optimized for maximum performance. The proposed all‐optical 4‐to‐2 encoder is based on a cascading set of all‐optical OR gates. The all‐optical 4‐to‐2 encoder's ON‐to‐OFF contrast ratio was 17.32 dB, while the response time was determined as 0.41 ps with a minimum footprint area of 105.18 µm 2 .

Connection density affects the behavior of functional brain network metrics.

Metal-insulator-metal (MIM) plasmonic metasurfaces provide a powerful platform for enhancing light-matter interactions; however, achieving simultaneous spectral tunability, fabrication reproducibility, and ultrasensitive detection remains a challenge. Here, we present the rational design, simulation, and lithographic fabrication of three distinct MIM metasurfaces (bowtie, honeycomb, and nanotriangle) optimized for plasmon-enhanced Raman spectroscopy (PERS). Finite-difference time domain (FDTD) simulations reveal localized surface plasmon resonances with electric field enhancement factors (EF) exceeding |E|2 ∼ 1600, supported by experimental reflection spectra and the fidelity of nanofabrication. Raman sensing of molecular probes (R6G, 4-ATP, 4-CTP) demonstrates analytical enhancement factors reaching 107 and detection limits as low as ∼10-15 M. This is made possible by the designed nanogap resonances, and the broadband localized surface plasmon resonance (LSPR) overlap with the excitation and scattering bands. Our findings establish the lithographically defined MIM metasurfaces as reliable, tunable, and ultrasensitive surface-enhanced Raman spectroscopy (SERS) platforms, making them suitable for next-generation portable chemical and biological sensing systems.

Recent studies have investigated various forms of the time-fractional and distributed-order nonlinear Klein–Gordon equations (KGEs). In this work, we focus on the nonlinear KGE involving distributed time derivatives and propose a novel numerical framework for its efficient and accurate solution. For cases where the nonlinear component in the KGE appears as sin ( ψ ( v ) = sin ⁡ ( v ) ), this particular form of the equation is commonly known as the Sine-Gordon equation, whose numerical solution holds significant importance. A Gaussian quadrature-based approach is employed to approximate the distributed derivative term, ensuring high precision in the numerical treatment. The proposed method combines the Generalized Moving Least Squares (GMLS) technique for spatial discretization with a first-order accurate finite difference approximation in the time domain. The experimental findings provide substantial evidence for the efficacy of our novel methodology in accurately capturing the complex dynamics of the equation, highlighting its potential for solving similar problems with distributed derivatives in diverse computational domains.

This paper investigates the complex dielectric properties, consisting of the real part, i.e., dielectric constant (ε') and the imaginary part, i.e., dielectric loss (ε''), of ripe papaya and navel orange fruit juices utilise the time domain reflectometry (TDR) technique. Filling a significant gap in existing literature, the study focuses on fruit juice complex dielectric properties measurement within the frequency (f) range 1GHz ≤ f ≤ 50 GHz to know the frequency dependence variation. Experimental measurements were conducted at 23 °C using fruit samples processed into filtered juices. The ε'-f curves reveal distinct dielectric behaviours between papaya and orange juices. Papaya exhibited significantly higher ε' up to 18 GHz, but this trend reversed thereafter. Similar opposing behaviors were observed in the ε''-f curves. Both juices displayed relaxation frequencies, with papaya peaking at 8 GHz and orange at 10.5 GHz. The study has the potential to advance food processing procedures, boost product standards and stimulate innovation across several sectors.

Ratiometric electrochemiluminescence (ECL) sensing is an effective strategy for improving signal reliability; however, most existing systems rely on dual luminophores or multiple electrochemical processes, which inevitably increase system complexity and compromise signal coherence. Herein, a fundamentally different ratiometric ECL paradigm was established based on a single luminophore capable of simultaneously generating conventional and afterglow ECL emissions. By engineering nitrogen defect-rich carbon nitride with defect electronic states, injected electrons can be temporarily stored during pulsed excitation and gradually released to sustain light emission, even after the applied potential is removed. This afterglow ECL process introduces an intrinsically low-background, time-resolved analytical signal with an identical emissive origin to conventional ECL. Based on the synchronous modulation of these homologous signals, an intrinsically self-referenced ratiometric ECL platform with exceptional stability and ultrahigh sensitivity was constructed. As a proof of concept, the strategy was successfully employed to quantify exosomal microRNA at attomolar levels in complex biological samples. Beyond this specific application, the proposed approach also represents a general single-component ratiometric ECL framework that can be utilized to expand the analytical scope of ECL sensing into the time domain.

ABSTRACT High spectral efficiency and robust wireless communication have been made possible by the rapid evolution of multiple‐input multiple‐output orthogonal frequency division multiplexing (MIMO‐OFDM) systems that have enhanced wireless communication in 5G and beyond. These developments allow exceptionally high spectral efficiency and provide communications that are remarkably reliable. Methods that are traditionally used to cut down peak‐to‐average power ratio (PAPR) are excessive clipping, selective mapping, and tone reservation. All of which, in one way or another, are incapable of pragmatic use due to the distortion of the signal, considerable complexity, and the requirement of additional information. In light of these limitations, this research introduces the heterogeneous edge‐enhanced circular dilated convolutional graph attention network (Het‐ECDCGAN)–based PAPR reduction framework. It draws on techniques from circular dilated convolutional neural networks (CD‐CNNs) and heterogeneous edge‐enhanced graph attention networks (HE‐GATs). The CD‐CNN successfully separates long‐range information from periodic peak structures in the time–domain, while the HE‐GAT utilizes graph‐based attention techniques to model inter‐subcarrier and inter‐antenna correlations. Binary Portia spider optimization is applied to optimize the performance of Het‐ECDCGAN. The designed framework can reach a bit error rate (BER) of up to 10 −4 , which guarantees good transmission and efficient PAPR reduction. In line with the validation of resilience at varying spectral efficiencies, modulation schemes, which include QPSK, 16‐QAM, and 64‐QAM schemes, are tested. All simulation experiments for MIMO‐OFDM signal generation, PAPR reduction, and performance evaluation of the proposed Het‐ECDCGAN framework are carried out using MATLAB.

Simulations of the absorption line shapes of various molecular systems provide insights into experimental data and can be used as tests for different models. The second-order complex-time dependent Redfield (ctR) theory has been shown to possess an excellent combination of accuracy and numerical efficiency. Nonetheless, there are some cases where improvements in its accuracy are desirable. In this study, we have developed a fourth-order extension of the ctR theory (ctR4). By assuming an exponential decomposition of the bath correlation function, we have derived analytical expressions, thus avoiding costly numerical integration in the time domain. Our results show that the ctR4 approach can provide higher quality results for the Debye spectral density, even in the case when the reorganization energies of different molecules are slightly different. On the other hand, for the Ohmic spectral density with exponential cutoff, the ctR4 line shapes are more accurate only for small reorganization energies, while for the super-Ohmic spectral density, the original second-order theory provides better results. Moreover, for some parameter values, the ctR4 line shapes can exhibit negative features. Therefore, the original ctR theory remains preferable in practical calculations.

BackgroundFrequent, sustained stress is linked to poor health and requires monitoring for early intervention. Electrocardiograms (ECG) are promising biomarkers because they can be recorded noninvasively and continuously using wearable devices. However, tracking stress with ECG is challenging because daily activities elicit responses similar to mental stress (MS), and various mental stimuli that individuals encounter complicate the use of machine learning (ML) models trained on a limited set of stressors.ObjectiveWe (1) evaluated the ability of ML models to distinguish MS episodes from a composite “no-stress” background, including rest and low- to moderate-intensity activities; (2) assessed their generalizability to new stressors and participants; and (3) tested robustness to lower sampling rates and fewer features, to explore their suitability for lightweight wearables.MethodsWe used a comprehensive ECG dataset sampled at 1000 hertz from 127 participants who underwent various mental stressors and engaged in diverse physical activities. A 30-second window was used to extract 55 features from time, frequency, nonlinear, and morphological domains. We trained a logistic regression (LR) model and an extreme gradient boosting (XGBoost) model, splitting the data into 60/20/20 for training, validation, and testing. Shapley additive explanation values were computed to explain model predictions. Additional analyses included leave-one-stressor-out; downsampling to 500, 250, and 125 hertz; a time-window sensitivity analysis; and reducing the number of features to as few as 5.ResultsXGBoost achieved an area under the receiver operating characteristic curve (AUROC) of 0.741 (95% CI 0.701‐0.783) and an area under the precision-recall curve (AUPRC) of 0.706 (95% CI 0.658‐0.753), compared with 0.724 (95% CI 0.678‐0.772) and 0.691 (95% CI 0.639‐0.742) for LR. The mean performance difference between XGBoost and LR was 0.017 for AUROC (95% CI 0.001‐0.032) and 0.015 for AUPRC (95% CI −0.001 to 0.037; clustered bootstrap analysis using 2000 participant-level resamples), suggesting that LR performs comparably to the nonlinear XGBoost model. Both models were robust to downsampling and feature reduction (10 features retained >93% of performance). Extending the analysis window to 60 seconds improved model performance across all sampling rates, highlighting a trade-off between rapid detection and overall performance. When evaluating discrimination from physical activity, models achieved acceptable specificity for light physical activity (XGBoost: 0.787; LR: 0.794) but poor specificity for moderate physical activity (XGBoost: 0.418; LR: 0.444). Both models generalized to most unseen stressors, although performance varied across stressors, with limited transfer to the social-evaluative stressor. Feature importance analysis revealed fuzzy entropy and frequency-based features as key predictors.ConclusionsML models can detect MS with high sensitivity and remain robust to lower sampling rates and fewer features. Generalization to novel stressors was stressor-dependent. Importantly, our results highlight challenges in distinguishing stress-related cardiac responses from those caused by physical exertion, revealing critical limitations of single-sensor ECG approaches for MS detection.

ABSTRACT Time domain airborne electromagnetic (AEM) surveying is a mature geophysical tool for imaging the Earth's shallow subsurface. It produces images of the electromagnetic conductivity structure of the earth, down to depths of a few hundred metres. The AEM method is fast, with rotary‐wing or fixed‐wing aircraft acquiring data at speeds of 100–300 km/h, making it an ideal near‐surface reconnaissance tool. The physics of the AEM method is sensitive primarily to the subsurface conductivity, which is influenced by a range of geological factors such as mineral content, porosity, and water content and chemistry. In addition, the inferred subsurface conductivity depends on the accurate measurement and modelling of airborne transmitter and receiver geometries – a challenging task given the speed of acquisition and variability of wind conditions during an acquisition flight. In this work, we present inferences of the subsurface conductivity over Lake Menindee, New South Wales, Australia, using data from test flights and various AEM systems over a 10‐year period (2014–2024). The lake storage has varied dramatically over this time, and the test flights have coincided with both high and low water levels. While this difference in storage volume undoubtedly influences the near‐surface conductivity, a remarkably consistent interpretation of the regional geology is possible regardless of the hydrologic conditions. While the upper 10 m of the modelled depth sections exhibit the greatest time‐variability in inferred electromagnetic conductivity, the hypothesis that lakebed near‐surface conductivity is significantly correlated with the lake water volume cannot robustly be established. We also provide some information‐theoretic calculations for each inversion result to aid in their quantitative comparison. The implications of our study are that subtle, shallow, hydrogeological changes are difficult to image with repeat AEM overflights from different systems. Conversely, we establish that different AEM systems with minimal extra processing robustly image the regional geo‐electric structure of the near surface, validated by known stratigraphy and associated geological information, as well as borehole conductivity logs.

A nonlinear ultrasonic time-domain identification method based on chaos sensitivity was proposed in this study. The Duffing chaotic system was introduced into the weak second harmonic identification to realize early detection and quantitative evaluation of fatigue damage in U71Mn steel. First, to ensure the reliability of nonlinear ultrasonic testing, a probe-pressure monitoring device was designed. Through pressure-stability experiments, 16 N was determined as the optimal pressure, which effectively suppresses contact nonlinearity interference and ensures coupling stability. Subsequently, the Duffing chaos detection system was established. The signal-system frequency-matching problem was resolved through time-scale transformation. Simultaneously, the issue of unknown initial phases was resolved using phase traversal compensation. Based on the chaotic system's sensitivity to specific frequency signals and immunity to noise, the amplitudes of the fundamental wave and second harmonics in the target signals were quantified to calculate the nonlinear coefficient. Experimental results demonstrate that the proposed method can extract these amplitudes directly in the time domain, thereby effectively overcoming the spectral leakage inherent in traditional frequency-domain methods. The nonlinear coefficient of U71Mn steel exhibits a "double-peak" characteristic as fatigue damage increases. Specifically, the first peak appears at approximately 50% of fatigue life, while the second occurs at approximately 80%. This phenomenon is closely correlated with the distinct stages of internal fatigue crack propagation, reflecting a complex damage-evolution mechanism. This study not only provides a novel method for the precise extraction of weak nonlinear signals but also establishes a critical theoretical and experimental foundation for accurate fatigue life prediction for U71Mn rail steel.

The purpose of the current study was to evaluate the potential beneficial effect and efficacy of two distinct high-intensity functional training (HIFT) protocols on cardio-respiratory fitness and functional movement performance in obese adolescents. Thirty-three obese adolescents were randomly divided into the HIFT combined with basketball group (HB, n = 11), the HIFT group (n = 11) and the control group (no structured training, n = 11), with a 12-week intervention (3 sessions/week, 45 min/training session). Body composition analysis, pulse wave velocity (PWV), flow-mediated dilation (FMD) measurement, heart rate variability (HRV) assessment, and maximum oxygen consumption (VO2max) test, and functional movement performance test (vertec jump, sitting anterior flexion, T-run, 300-yard shuttle run, Y-balance) were employed to evaluate their fitness. Both interventions significantly reduced body weight, BMI, and fat mass, while increasing water content. Additionally, HIFT could increase the inorganic salt content, protein content, and muscle mass, while decreased BMIz significantly. In terms of cardiovascular parameters, both interventions substantially reduced blood pressure. However, HIFT could decrease the cfPWV. Additionally, both interventions significantly decreased the AIx and AIx@HR75, while improving SEVR and FMD. For HRV indices, both interventions decreased the HR, increased the time domain indices (RMSSD, PNN50, SDNN and NN50) and TP of obese adolescents, while also elevating HF and reduced LF/HF ratio compared to the control group. We also found that both interventions could boost VO2max and functional movement performance. This study demonstrated HB and HIFT exerted comparable efficacy in improving the body composition parameters, cardiovascular function, and functional movement performance in obese adolescents, possibly by mitigating arterial stiffness, balancing autonomic activity, enhancing respiratory fitness and optimizing hemodynamics.

Dual-parameter ultrasonic guided wave packet characterization for ice thickness prediction and excitation signal optimization.

This study investigates the role of artificial intelligence (AI) tokens in dynamic interactions, diversification, and hedging capabilities, in relation to non-fungible tokens (NFTs), decentralised finance (DeFi) tokens, and renewable energy assets. Using the Time-Varying Parameter Vector Autoregressive (TVP-VAR) model, we examine return, volatility, and higher-order spillovers across both time and frequency domains. The results show that NFTs serve as persistent channels for the transmission of return and volatility shocks, driven by their speculative nature. AI and renewable tokens primarily absorb systemic risk due to their lower liquidity and niche adoption. DeFi tokens play flexible roles, shifting between transmitters and receivers across market regimes. The results demonstrate asset-specific idiosyncrasies and that volatility spillovers are generally stronger than return spillovers. Frequency-domain analysis highlights that digital tokens dominate short-term spillovers, while renewable assets absorb shocks across horizons. However, higher-order moment results reveal that extreme risk linkages shift transmission channels. Our results also confirm that oil market (OVX) shocks drive short-term return connectedness, CBOE volatility (VIX) volatility, and policy uncertainty (EPU) significantly impact return linkages. The results of our portfolio analysis show that AI tokens form the core of diversification, NFTs provide short-term speculative hedging, and renewable assets, particularly solar-linked tokens, act as low-cost stabilisers, underscoring the need for active rebalancing under different market regimes. These findings provide meaningful implications for policymakers, regulators, and portfolio managers for strengthening systemic risk oversight and considering asset-specific idiosyncrasies in investment strategies.

ABSTRACT This study investigates the volatility spillover between tourism tokens and travel and tourism (T&T) subsector indices, using Diebold and Yilmaz (2012) approach in both the static and dynamic time domains. The study’s sample period is the daily data from December 2021 to September 2024 for three major tourism tokens and six T&T subsector indices. The empirical findings suggest a weak and time-varying interdependency between tourism tokens and T&T subsectors. The results also reveal that tourism tokens offer portfolio diversification and enhance hedging performance. This study thus provides useful insights for individual investors, portfolio managers and policymakers.