Search Results (250)

Background: Understanding the origin and natural organization of early infant vocalizations is important for predicting communication and language abilities in later years. The very frequent production of speech-like vocalizations (hereafter “protophones”), occurring largely independently of interaction, is part of this developmental process. Objectives: This study aims to investigate the gap durations (time intervals) between protophones, comparing typically developing (TD) infants and infants later diagnosed with autism spectrum disorder (ASD) in a naturalistic setting where endogenous protophones occur frequently. Additionally, we explore potential age-related variations and sex differences in gap durations. Methods: We analyzed ~1500 five min recording segments from longitudinal all-day home recordings of 147 infants (103 TD infants and 44 autistic infants) during their first year of life. The data included over 90,000 infant protophones. Human coding was employed to ensure maximally accurate timing data. This method included the human judgment of gap durations specified based on time-domain and spectrographic displays. Results and Conclusions: Short gap durations occurred between protophones produced by infants, with a mode between 301 and 400 ms, roughly the length of an infant syllable, across all diagnoses, sex, and age groups. However, we found significant differences in the gap duration distributions between ASD and TD groups when infant-directed speech (IDS) was relatively frequent, as well as across age groups and sexes. The Generalized Linear Modeling (GLM) results confirmed these findings and revealed longer gap durations associated with higher IDS, female sex, older age, and TD diagnosis. Age-related differences and sex differences were highly significant for both diagnosis groups. Full article

Autonomous Unmanned Surface Vehicle (USV) operations in complex ocean engineering scenarios necessitate robust navigation, guidance, and control technologies. These systems require reliable sensor-based object detection and efficient, safe, and energy-aware path planning. To address these multifaceted challenges, this paper proposes a novel synergistic AI framework. The framework integrates (1) a novel adaptation of the Swin-Transformer to generate a dense, semantic risk map from raw visual data, enabling the system to interpret ambiguous marine conditions like sun glare and choppy water, enabling real-time environmental understanding crucial for guidance; (2) a Transformer-enhanced A-star (T-ASTAR) algorithm with spatio-temporal attentional guidance to generate globally near-optimal and energy-aware static paths; (3) a domain-adapted TD3 agent featuring a novel energy-aware reward function that optimizes for USV hydrodynamic constraints, making it suitable for long-endurance missions tailored for USVs to perform dynamic local path optimization and real-time obstacle avoidance, forming a key control element; and (4) CUDA acceleration to meet the computational demands of real-time ocean engineering applications. Simulations and real-world data verify the framework’s superiority over benchmarks like A* and RRT, achieving 30% shorter routes, 70% fewer turns, 64.7% fewer dynamic collisions, and a 215-fold speed improvement in map generation via CUDA acceleration. This research underscores the importance of integrating powerful AI components within a hierarchical synergy, encompassing AI-based perception, hierarchical decision planning for guidance, and multi-stage optimal search algorithms for control. The proposed solution significantly advances USV autonomy, addressing critical ocean engineering challenges such as navigation in dynamic environments, object avoidance, and energy-constrained operations for unmanned maritime systems. Full article

This paper focuses on the switching and modulation techniques of terahertz waves, develops ${\mathrm{VO}}_2$ thin-film materials with an SPP structure, and uses terahertz time-domain spectroscopy (THz-TDS) to study the semiconductor–metal phase transition characteristics of ${\mathrm{VO}}_2$ thin films, especially the photoinduced semiconductor–metal phase transition characteristics of silicon-based ${\mathrm{VO}}_2$ thin films. The optical modulation characteristics of silicon-based ${\mathrm{VO}}_2$ thin films to terahertz waves under different light excitation modes, such as continuous light irradiation at different wavelengths and femtosecond pulsed laser irradiation, were analyzed. Combining the optical modulation characteristics of silicon-based ${\mathrm{VO}}_2$ thin films with the filtering characteristics of SPP structures, composite structures of ${\mathrm{VO}}_2$ thin films with metal hole arrays, composite structures of ${\mathrm{VO}}_2$ thin films with metal block arrays, and silicon-based ${\mathrm{VO}}_2$ microstructure arrays were designed. The characteristics of this dual-function device were tested experimentally. The experiment proves that the ${\mathrm{VO}}_2$ film material with an SPP structure has a transmission rate dropping sharply from 32% to 1% under light excitation; the resistivity changes by more than six orders of magnitude, and the modulation effect is remarkable. By applying the SPP structure to the ${\mathrm{VO}}_2$ material, the material can simultaneously possess modulation and filtering functions, enhancing its optical performance in the terahertz band. Full article

Interpreting complex clinical time series is vital for patient safety and care, as it is both essential for supporting accurate clinical assessment and fundamental to building clinician trust and promoting effective clinical action. In complex time series analysis, decomposing a signal into meaningful underlying components is often a crucial means for achieving interpretability. This process is known as time series disentanglement. While deep learning models excel in predictive performance in this domain, their inherent complexity poses a major challenge to interpretability. Furthermore, existing time series disentanglement methods, including traditional trend or seasonality decomposition techniques, struggle to adequately separate clinically crucial specific components: static patient characteristics, condition trend, and acute events. Thus, a key technical challenge remains: developing an interpretable method capable of effectively disentangling these specific components in complex clinical time series. To address this challenge, we propose CoTD-VAE, a novel variational autoencoder framework for interpretable component disentanglement. CoTD-VAE incorporates temporal constraints tailored to the properties of static, trend, and event components, such as leveraging a Trend Smoothness Loss to capture gradual changes and an Event Sparsity Loss to identify potential acute events. These designs help the model effectively decompose time series into dedicated latent representations. We evaluate CoTD-VAE on critical care (MIMIC-IV) and human activity recognition (UCI HAR) datasets. Results demonstrate successful component disentanglement and promising performance enhancement in downstream tasks. Ablation studies further confirm the crucial role of our proposed temporal constraints. CoTD-VAE offers a promising interpretable framework for analyzing complex time series in critical applications like healthcare. Full article

Time-domain (TD) diffuse reflectance can be modeled using diffusion theory (DT) to non-invasively estimate optical transport coefficients of biological media, which serve as markers of tissue physiology. We employ an optimized N-layer DT solver in cylindrical geometry to reconstruct optical coefficients of bilayered media from TD reflectance generated via Monte Carlo (MC) simulations. Optical properties for 384 bilayered tissue models representing human head or limb tissues were obtained from the literature at three near-infrared wavelengths. MC data were fit using the layered DT model to simultaneously recover transport coefficients in both layers. Bottom-layer absorption was recovered with errors under 0.02 cm⁻¹, and top-layer scattering was retrieved within 3 cm⁻¹ of input values. In contrast, recovered bottom-layer scattering had mean errors exceeding 50%. Total hemoglobin concentration and oxygen saturation were reconstructed for the bottom layer to within 10 μM and 5%, respectively. Extracted transport coefficients were significantly more accurate when obtained using layered DT compared to the conventional, semi-infinite DT model. Our results suggest using improved theoretical modeling to analyze TD reflectance analysis significantly improves recovery of deep-layer absorption. Full article

Digital predistortion and nonlinear behavioral modeling of power amplifiers (PA) have been the subject of intensive research in the time domain (TD), in contrast with the limited number of works conducted in the frequency domain (FD). However, the adoption of orthogonal frequency division multiplexing (OFDM) as a prevalent modulation scheme in current wireless communication standards provides a promising avenue for employing an FD approach. In this work, a procedure to model nonlinear distortion in wireless OFDM systems in the frequency domain is demonstrated for general model structures based on a sparse Bayesian learning (SBL) algorithm to identify a reduced set of regressors capable of an efficient and accurate prediction. The FD-SBL algorithm is proposed to first identify the active FD regressors and estimate the coefficients of the PA model using a given symbol, and then, the coefficients are employed to predict the distortion of successive OFDM symbols. The performance of this proposed FD-SBL with a validation NMSE of $-47$ dB for a signal of 30 MHz bandwidth is comparable to $-46.6$ dB of the previously proposed implementation of the TD-SBL. In terms of execution time, the TD-SBL fails due to excessive processing time and numerical problems for a 100 MHz bandwidth signal, whereas the FD-SBL yields an adequate validation NMSE of −38.6 dB. Full article

We demonstrate a novel bidirectional mode-locked ultrafast fiber laser based on an asymmetric dual-cavity architecture that enables freely tunable repetition rate differentials at the kilohertz level, while maintaining inherent common-mode noise suppression through precision thermomechanical stabilization. Through cascaded amplification and nonlinear temporal compression, we obtained bidirectional pulse durations of 33.2 fs (clockwise) and 61.6 fs (counterclockwise), respectively. The developed source demonstrates exceptional capability for asynchronous optical sampling applications, particularly in enabling the compact implementation of real-time measurement systems such as terahertz time-domain spectroscopy (THz-TDS) systems. Full article

Determining the technological quality of fresh meat pieces is essential in the meat industry to ensure the production of high-quality products. For this purpose, nuclear magnetic resonance (NMR) is a non-destructive and non-invasive technique that appears as an alternative to traditional methodologies. The objective of this work is to determine the potential of magnetic resonance imaging (MRI) and time-domain (TD-NMR) relaxometry for determining the physicochemical characterization of fresh hams with different industrial destinations (both fresh and cured products, such as dry-cured ham). For this study, the biceps femoris, semimembranosus, and semitendinosus muscles of 20 fresh hind legs from white pigs, classified into four categories according to their fat content, were analyzed. The semitendinosus muscle was selected as a model, and positive and negative correlations were obtained between different physicochemical parameters and the longitudinal (T1) and transverse (T2) relaxation times obtained by MRI and TD-NMR. Regression models using T1 and T2 were also developed to predict the muscle water-holding capacity (WHC) and drip loss, using high, medium, and low magnetic field NMR (R² > 0.80). Therefore, MRI and TD-NMR could be considered as highly suitable and accurate non-destructive techniques for the WHC determination in the meat industry. Full article

Keeping drinking water safe is a critical aspect of protecting public health. Traditional laboratory-based methods for evaluating water potability are often time-consuming, costly, and labour-intensive. This paper presents a comparative analysis of four transformer-based deep learning models in the development of automatic classification systems for water potability based on physicochemical attributes. The models examined include the enhanced tabular transformer (ETT), feature tokenizer transformer (FTTransformer), self-attention and inter-sample network (SAINT), and tabular autoencoder pretraining enhancement (TAPE). The study utilized an open-access water quality dataset that includes nine key attributes such as pH, hardness, total dissolved solids (TDS), chloramines, sulphate, conductivity, organic carbon, trihalomethanes, and turbidity. The models were evaluated under a unified protocol involving 70–15–15 data partitioning, five-fold cross-validation, fixed random seed, and consistent hyperparameter settings. Among the evaluated models, the enhanced tabular transformer outperforms other models with an accuracy of 95.04% and an F1 score of 0.94. ETT is an advanced model because it can efficiently model high-order feature interactions through multi-head attention and deep hierarchical encoding. Feature importance analysis consistently highlighted chloramines, conductivity, and trihalomethanes as key predictive features across all models. SAINT demonstrated robust generalization through its dual-attention mechanism, while TAPE provided competitive results with reduced computational overhead due to unsupervised pretraining. Conversely, FTTransformer showed limitations, likely due to sensitivity to class imbalance and hyperparameter tuning. The results underscore the potential of transformer-based models, especially ETT, in enabling efficient, accurate, and scalable water quality monitoring. These findings support their integration into real-time environmental health systems and suggest approaches for future research in explainability, domain adaptation, and multimodal fusion. Full article

Precision thickness control in new energy electrode coatings is a critical determinant of battery performance characteristics. This study presents a non-destructive inspection methodology employing terahertz time-domain spectroscopy (THz-TDS) to achieve high-precision coating thickness measurement in lithium iron phosphate (LFP) battery manufacturing. Industrial THz-TDS systems mostly adopt fixed threshold filtering or Fourier filtering, making it disssssfficult to balance noise suppression and signal fidelity. The developed approach integrates three key technological advancements. Firstly, the refractive index of the material is determined through multi-peak amplitude analysis, achieving an error rate control within 1%. Secondly, a hybrid signal processing algorithm is applied, combining an optimized Savitzky–Golay filter for high-frequency noise suppression with an enhanced sinc function wavelet threshold technique for signal fidelity improvement. Thirdly, the time-of-flight method enables real-time online measurement of coating thickness under atmospheric conditions. Experimental validation demonstrates effective thickness measurement across a 35–425 μm range, achieving a 17.62% range extension and a 2.13% improvement in accuracy compared to conventional non-filtered methods. The integrated system offers a robust quality control solution for next-generation battery production lines. Full article

Polyamides (PAs) are widely used as barrier materials in offshore oil and gas (O&G) equipment due to their mechanical strength and chemical resistance. However, long-term exposure to hydrogen sulfide scavengers (H₂S-SCVs) may significantly affect their physicochemical properties. Previous studies using thermal analysis and ¹H time-domain NMR (¹H TD-NMR) suggest that ethoxylated H₂S-SCVs impose molecular constraints, increasing the glass transition temperature (T_g) and reducing chain mobility above T_g. The present study builds upon these findings using a multi-technique analytical approach, including FTIR, Raman, ¹H DQ-TD-NMR, and ¹³C solid-state NMR (ssNMR), to provide a more comprehensive understanding of the molecular alterations in PA materials. The results clearly demonstrate that H₂S-SCV exposure leads to the progressive exudation of plasticizers from the PA matrix. This plasticizer loss is a key factor contributing to the observed shift in Tg and the reduction in molecular mobility. ¹H DQ-TD-NMR data confirmed an increase in the density of dynamically constrained chains over time and allowed for the characterization of heterogeneity in these constraints throughout the PA matrix. Moreover, ¹³C ssNMR spectra revealed the presence of immobilized H₂S-SCV chemical groups within the polymer matrix, strongly supporting the early statement that the mobility constraints observed in ¹H DQ-TD-NMR are associated with the formation of crosslinks induced by the H₂S-SCV: H₂S-SCV acts as a crosslinking agent. Taken together, our findings indicate that both plasticizer loss and H₂S-SCV-induced crosslinking contribute significantly to the microstructural evolution of PAs when exposed to ethoxylated H₂S-SCVs, offering important insights into their degradation mechanisms and long-term behavior in aggressive operational environments. Full article

Satellite Internet is the key to integrated air–space–ground communication, while the design of waveforms with high spectrum efficiency is an intrinsic requirement for high-speed data transmission in satellite Internet. Time-domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) technology can significantly improve spectrum utilization efficiency by using PN sequences instead of traditional CP cyclic prefixes. However, it also leads to time-domain aliasing between PN sequences and data symbols, posing a serious challenge to channel estimation. To solve this problem, a compressive sensing-based coding iterative channel estimation method for the TDS-OFDM system is proposed in this paper. This method innovatively combines compressive sensing channel estimation technology with the Reed–Solomon low-density parity-check cascade coding (RS-LDPC) scheme, and achieves performance improvements as follows: (1) Construct the iterative optimization mechanism for the compressive sensing algorithm and equalization feedback loop. (2) RS-LDPC cascaded coding is employed to enhance the anti-interference and error correction capability of system. (3) Design the recoding link of error-corrected data to improve the accuracy of sensing matrix. The simulation results demonstrate that compared with conventional methods, the proposed method can obviously converge on the mean squared errors (MSEs) of channel estimation and significantly reduce the bit error rate (BER) of the system. Full article

Multilayer composite materials often develop internal defects at varying depths due to manufacturing and environmental factors. Traditional planar scanning methods lack the ability to pinpoint defect locations in depth. This study proposes a terahertz time-domain spectroscopy (THz-TDS)-based defect detection method using continuous wavelet transform (CWT) to convert spectral signals into time-frequency images. These are analyzed by the ResNet18 model combined with a support vector machine (SVM) classifier. Comparative experiments with four classical deep learning models and three classifiers show that the Residual Network with 18 layers (ResNet18-SVM) approach achieves the highest accuracy of 98.56%, effectively identifying three types of defects. The results demonstrate the method’s strong feature extraction, depth resolution, and its potential for nondestructive evaluation of multilayer structures. Full article

This study explores the relationship between nutrient concentration (NC) and epidermal thickness (d) of the leaves of hydroponically grown Brassica rapa and its attenuation coefficients (m) using portable Time-Domain Optical Coherence Tomography (TD-OCT), which is a non-invasive imaging technique that uses low-coherence interferometry to generate axial scans of plants’ leaves by measuring the time delay and intensity of backscattered light. The portable TD-OCT system in this study has an axial and lateral resolution of 7 m and 3 m, respectively, a scanning depth of 12 mm, and a 1310 nm Super Luminescent Diode (SLD). Several studies suggest that the differences in d and m are related to nutritional, physiological, and anatomical status. The study used the Kratky method, a simple non-circulating hydroponic system, to cultivate Brassica rapa with varying NC (25%, 50%, 75%, 100% (control), and 125%). Each treatment group used two plants. The TD-OCT sample probe was placed on a fixed holder and was oriented vertically so that light was directed downward onto the leaf’s surface to obtain the depth profile (A-scan). The distance between the probe and the leaf was adjusted to obtain the optimum interference signal. Five averaged A-scans were obtained per leaf on the 7th, 18th, and 21st days post nutrient exposure. The logarithm of the averaged A-scan is linearly fitted to extract m. The results showed a positive correlation between NC and m, which suggests that plants produce more chlorophyll and develop denser cells and increase m. There was no correlation obtained between NC and d. The study demonstrates the potential of TD-OCT as a non-destructive tool for assessing plant health and monitoring growth dynamics in hydroponic systems and m as a sensitive indicator of plant health as compared to d. The continued exploration of TD-OCT applications in agriculture can contribute to improving crop management strategies and promoting sustainable food production practices. Full article

The physical stability, molecular mobility, and appearance of nanocrystalline resveratrol in a polyvinylpyrrolidone (PVP) matrix were investigated. Two formulations with resveratrol loadings of 30% and 50% were prepared and characterized using powder X-ray diffraction (PXRD) and time-domain nuclear magnetic resonance (TD-NMR). Samples were studied over time (up to 300 days post-preparation), across temperatures (80–300 K), and under varying humidity conditions (0% and 75% relative humidity). The results demonstrate that the 30% resveratrol–PVP sample is a homogeneous amorphous solid dispersion (ASD), while the 50% resveratrol–PVP sample contained resveratrol nanocrystals measuring about 40 nm. NMR measurements and molecular dynamics (MD) simulations revealed that incorporation of resveratrol into the polymer matrix modifies the system’s dynamics and mobility compared to the pure PVP polymer. Additionally, MD simulations analyzed the hydrogen bonding network within the system, providing insights for a better understanding of the physical stability of the ASD under different conditions. Full article