Wave-to-Wire (W2W) modeling simulates the whole operation process of wave energy converters (WECs), which plays a pivotal role in the systematic design and optimization of WECs. Existing W2W models are predominantly constructed based on time-domain (TD) analysis to coherently incorporate relevant nonlinearities. However, TD models require a high computational cost, which hinders the design iterations of WECs. As a newly emerging alternative approach, spectral-domain (SD) modeling has demonstrated the applicability of describing the W2W process while efficiently covering nonlinear effects through statistical linearization. This study aims to develop an SD W2W modeling approach for WECs coupled with a gearbox and rotary generator. The application of the proposed model is exemplified in two case studies: (1) a point absorber with a rack-pinion system and a rotary generator; (2) a flap-type WEC with a revolving gearbox and a rotary generator. The simulation results obtained by the SD W2W model are compared against a higher-fidelity nonlinear TD W2W model to verify its accuracy across a variety of sea states. A good agreement between the two modeling approaches is observed, in which the maximum relative error is below 7 % with regard to the estimation of important system outputs. Meanwhile, the computational efficiency of the SD W2W model is thousands of times higher than the TD modeling approach. • The spectral-domain modeling can be used to simulate the wave-to-wire process of wave energy converters with geared generators. • The extended spectral-domain modeling is much more efficient manner than time-domain approaches. • The extended statistical linearization maintains adequate accuracy in operational regions.

Deep liquid neural network for prediction of weather-impacted flight delay

Diabetes is a major global health challenge, with many individuals remaining undiagnosed due to the limitations of traditional screening methods. Artificial intelligence (AI)-based electrocardiogram (ECG) analysis offers a promising, non-invasive approach for the early detection of diabetes. This systematic review aims to critically evaluate machine learning (ML) and deep learning (DL) models developed for non-invasive prediction of diabetes and prediabetes using ECG signals. A comprehensive literature search was conducted across PubMed, Embase, Web of Science, IEEE Xplore, and ACM Digital Library in accordance with PRISMA 2020 guidelines. Twenty-five studies met the inclusion criteria. Extracted data included ECG input types, model architectures, preprocessing methods, feature sets, validation strategies, and performance metrics. Most studies used small, single-site, cross-sectional datasets, with sample sizes ranging from 24 to over 190,000 individuals. ECG preprocessing methods varied widely, including filtering, normalization, and decomposition. Features were extracted from time, frequency, morphological, and non-linear domains, though formal feature selection was applied inconsistently. ML and DL models reported high internal accuracy (>90%) but most lacked external validation and subgroup performance assessments. Notably, no study specifically focused on rural or underserved populations, and only one provided open-source code. AI-based ECG analysis demonstrates strong potential for detecting diabetes; however, current research is limited by generalizability issues, lack of standardized methods, poor external validation, and insufficient transparency. Future studies should prioritize rigorous validation, reproducibility, fairness audits, and applications in rural and underserved settings to ensure equitable and clinically viable deployment of these models.

Photoluminescence of silicon nanorods via plasmonic gold nanopore arrays.

The extensive integration of inertia-less renewable energy sources, such as solar photovoltaic (PV) systems, has introduced several stability challenges. The virtual synchronous generator (VSG) has emerged as a promising solution for addressing low-inertia issues. However, the integration of numerous VSG-controlled PV plants into the power system may introduce additional oscillations due to the dynamic interaction between VSG and PV inverter controls. In this paper, a large number of utility-scale VSG-controlled PV units are deployed to analyze their effect on the small-signal oscillations. Subsequently, an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula>-based VSG parameters optimization is proposed to enhance the small-signal stability. A systematic modal analysis procedure is also established to define the highest and lowest levels for adaptive variation of VSG parameters to further improve the large-signal stability. These optimized and adaptive features are combined in the form of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula>-optimization reinforced adaptive (HORA) VSG control to improve the overall stability of the PV-dominated grid. The performance of the proposed HORA VGS control is compared with non-adaptive and conventional adaptive VSG control techniques. The studies are conducted on the IEEE 68-bus, 16-machine power system by means of both frequency and time domain analyses through various case studies on the MATLAB/Simulink platform.

A deep learning framework for heat demand forecasting using time–frequency representations of decomposed features

The aim was to examine the effect of sleep restriction, fragmentation, and extension on athletic and academic performance, cognition, autonomic function, and mood of youth athletes. Sixteen pre-elite youth athletes completed four sleep experimental trials in a randomised-control design. Each trial included sleep restriction to 4 h (RES), sleep fragmentation by periodic waking throughout the night (FRAG), sleep extension to 10 h (EXT), or control/normal night sleep (CONT). The following day, testing included resting heart rate variability (HRV); exercise tests, Stroop task, and mood and wellness questionnaires. Time domain HRV data indicate no differences between conditions (P = 0.126 – 0.945, d = 0.11 – 0.67), while frequency domain indices indicate resting very low frequency (VLF) % contribution higher in RES compared to CONT and RES (P = 0.05, d = 0.86 – 0.96). Stroop reaction times were slower and accuracy lower in FRAG (P = 0.001–0.009; d = 1.90 – 2.55), while maths results were not different between conditions (P = 0.91; d = 0.13 – 0.58). There were no differences between conditions for sprint times, agility times, and vertical jump height (P = 0.38–0.98; d = 0.02 – 0.35). Throwing reaction task accuracy was lower in FRAG compared to CONT and RES (P = 0.006–0.05; d = 0.8) with moderate to large effects noted for throwing duration ( d = 0.43 – 1.21). Scores for ‘positive’ feelings were lower (P = 0.02 - 0.001; d = 0.51 – 0.99) and ‘negative’ feelings were increased for RES and FRAG compared to CONT (P = 0.003–0.08; d = 1.01 – 1.84). Perceived wellness was lower for RES compared to CONT (P = 0.016 – 0.0001; d = 0.9 – 2.34). Sleep fragmentation and restriction had no significant effects on anaerobic exercise performance or academic tests, but significant decrements were evident in mood states, autonomic regulation, and accuracy during complex cognition tasks.

Coupled hydroelastic responses of horizontal bending and torsion of a flexible large ship using a time domain method

A time domain method for the calculation of motions and wave loads of hovercraft in waves

Vectorcardiogram (VCG) signal compression is very much in demand in the present-day scenario due to the increasing number of cardiac patients. Hence, in this paper, a new technique is proposed that compresses VCG signal by optimizing the tunable quality wavelet transform (TQWT) parameters. The noise in VCG signal is firstly removed by applying a Savitzky-Golay filter, and then passing noise-free signal to an optimization algorithm that optimizes the TQWT parameters, and obtains the frequency domain signal. This signal is then quantized through dead-zone quantization and processed by a lossless compression mechanism: run-length encoding (RLE) to improve the compression ratio & encode the signal. This compressed signal is reconstructed by Inverse RLE to obtain the decoded signal. Inverse of TQWT is applied to get the reconstructed signal back from the transformed frequency domain to time domain. The parameters of TQWT, especially theQandR, are optimized to get the highestCRat lowest percent root-mean-square-difference(PRD)with best reconstruction quality and least distortions, along with acceptable values of signal-to-noise-ratio(SNR), quality score(QS), andSimilaritywith lowest mean-square-error(MSE). The comparative analysis of different optimization methods indicates that the sparse-particle swarm optimization is best among all the approaches for the tuning of parameters in TQWT for VCG signal compression and reconstruction achieving aCRof 48.18 at aPRDof 3.68,SNRof 29.39,QSof 15.71, similarity of 0.99845,MSEof 0.00016, withQvalue of 2.04307 andRvalue of 1.20568 withcomputational timeof 4.48508 s.

In this paper, meshfree collocation with fast-moving least-squares reproducing kernel was implemented to write down and execute numerical solutions for some applications in elastostatics and elastodynamics. The spatial discretization using meshfree collocation method was carried out on the equilibrium differential equations of elastostatics and elastodynamics and the corresponding boundary conditions. The resulting discrete forms were solved for benchmark problems in the one- and two-dimensional cases. In each case, a convergence study was conducted to ascertain the utility and efficacy of the developed solutions. For elastodynamics, the time domain, however, was discretized using the Newmark beta time-integration scheme. The latter combination was implemented to solve suitable benchmark problems in the one-dimensional and two-dimensional cases. In each case, a stability study was conducted to demonstrate, again, the method’s efficacy in handling elastodynamic problems.

Abstract X-ray Thomson scattering (XRTS) constitutes an essential technique for diagnosing material properties under extreme conditions, such as high pressures and intense laser heating. Time-dependent density functional theory (TDDFT) is one of the most accurate available ab initio methods for modeling XRTS spectra, as well as a host of other dynamic material properties. However, strong thermal excitations, along with the need to account for variations in temperature and density as well as the finite size of the detector significantly increase the computational cost of TDDFT simulations compared to ambient conditions. In this work, we present a broadly applicable method for optimizing and enhancing the efficiency of TDDFT calculations. Our approach is based on a one-to-one mapping between the dynamic structure factor and the imaginary time density–density correlation function, which naturally emerges in Feynman’s path integral formulation of quantum many-body theory. Specifically, we combine rigorous convergence tests in the imaginary time domain with a constraints-based attenuation of narrow-band fluctuations to improve the efficiency of TDDFT modeling without the introduction of any significant bias. As a result, we can report a speed-up by up to an order of magnitude, thus substantially reducing the burden of computational cost required for XRTS analysis.

As key transmission components in mechanical systems, gearboxes often operate under harsh environments and heavy-load conditions, making them susceptible to various types of damage. In recent years, built-in encoder signals have attracted increasing attention for rotating machinery health monitoring due to their low cost, ease of acquisition, and direct correlation with rotational motion. However, fault-related features in encoder signals are usually weak and easily submerged by strong harmonic interference and noise, posing significant challenges for accurate fault identification and feature extraction. To address this issue, this article proposes a weighted bi-domain sparse decomposition (WBSD) model for encoder signal analysis and fault diagnosis of gearboxes. The proposed WBSD model exploits the distinct morphological characteristics of fault-induced impulses and interference components in both the time and frequency domains. Specifically, two dedicated nonconvex regularization terms are constructed to enforce periodic group sparsity of fault impulses in the time domain and spectral sparsity of harmonic interference in the frequency domain by introducing weighted coefficients, periodic binary vectors, and nonconvex penalty functions, thereby enabling accurate separation and sparse representation of fault features. Furthermore, an efficient iterative solving algorithm is developed for the WBSD model by integrating the alternating direction method of multipliers with the majorization–minimization method. Experimental results obtained from both simulated signals and real encoder signals collected from a planetary gearbox test platform demonstrate that the proposed WBSD model consistently outperforms comparative methods in extracting weak fault impulses under strong noise and interference, confirming its effectiveness and practical applicability for fault diagnosis of gearboxes.

ABSTRACT Acrylonitrile–butadiene–styrene (ABS) cantilever beams were reinforced with carbon black (CB) at 0–2 wt% and fabricated by mechanical extrusion (MEX)‐based additive manufacturing to improve vibration damping without compromising strength. Free‐decay responses were processed with three complementary estimators—logarithmic decrement (time domain), half‐power bandwidth (frequency domain), and an envelope‐fit that provides an analytic standard error—and the per‐run damping ratios were fused by precision (inverse‐variance) weighting to obtain a single with 68% confidence intervals. Composition‐level results show a clear optimum at 0.3 wt% CB, where the fused damping ratio increased from 5.81 × 10 −3 (pure ABS) to 7.75 × 10 −3 (≈+33%), while the ultimate tensile strength rose from 9.83 to 18.12 MPa (≈+84%). At 1 wt%, the damping remained elevated but strength decreased; at 2 wt% both metrics declined, consistent with agglomeration observed in scanning electron microscope images. A strength–damping Pareto view highlights 0.3 wt% as a practical composition window for balanced performance in MEX ABS/CB parts. The workflow—multi‐estimator analysis with uncertainty‐aware fusion—provides reproducible damping estimates from short free‐decay records and can be applied to other printed polymers and fillers.

Abstract Distinguishing between microseismic (MS) events and blasting interference is very critical. However, it remains a challenging issue in mine MS monitoring, mainly due to waveform similarity, low signal-to-noise ratios (SNR), and dynamically changing propagation environments. Therefore, to meet the core demand of accurately identifying MS} signals from mines and blasting interference signals, we propose a comprehensive dynamic weighted distributed classification model. First, we constructed physically interpretable core features by extracting the essential differences between the two types of signals in the time, frequency, energy, and spatial domains. {We then performed a three-dimensional validation, which includes spatial attenuation, magnitude sensitivity, and detection threshold analysis to verify the authenticity and reliability of feature discrimination between MS and interference signals. Second, we introduced a distributed Frank-Wolfe logistic regression (DFW-LR) model with a reinforced learning dynamic feature weighting strategy (RL-DW). We initialized the feature weights using the Fisher Discriminant Ratio (FDR) and updated them dynamically based on the F1 score. The model parameters are then optimized via a distributed Frank-Wolfe algorithm}. Finally, experiments are conducted based on two real mine datasets. The proposed method achieves an average accuracy of 0.941 and an average F1 score of 0.962, outperforming fixed-weight logistic regression, Support Vector Machine (SVM), and a Multilayer Perceptron (MLP). Our proposed method maintains physical interpretability and offers a lower computational cost compared to existing deep learning models. The proposed framework provides a reliable, explainable, and deployable solution for MS monitoring and early warning in underground environments.

Mobile ambulatory cardiac telemetry (MCOT) use has increased over time; however, data on the prevalence and the impact of emergent arrhythmia notifications with MCOT remains unclear. We sought to determine the prevalence and clinical impact of emergent arrhythmia events in patients undergoing MCOT monitoring. We also analyzed the efficiency of the emergent notification process. We analyzed 8404 consecutive patients from two centers who were prescribed Philips MCOT (K153473) over a 28-month period (September 2018-January 2021). Participants meeting emergent notification criteria were included. The primary outcome was any unscheduled provider intervention after the emergent notification. We also analyzed several time domains of the provider notification process. A total of 122 patients (1.45%) satisfied emergent notification criteria during the study period. The median notification time from arrhythmia onset to provider notification was 42 min. Physician review of the arrhythmia notifications showed agreement with the monitoring technician diagnosis in 102/122 (83.6%). An emergent notification resulted in an unscheduled follow-up visit in 104/122 (85.2%) patients. Time from arrhythmia event to unscheduled follow up visit was < 24 h in 88/104 (84.6%), 24-72 h in 9/104 (8.7%) and > 72 h in 7/104 (6.7%). In 33 patients (27%), emergent notifications resulted in unscheduled interventions including: device implantation (24), ablation (8), and electrical cardioversion (4). Emergent arrhythmias events recorded during ambulatory telemetry monitoring resulted in unscheduled patient contact in 85% of cases and procedures in 27% of cases. The monitoring notification process was efficient, with median time from arrhythmia onset to provider notification of 42 min.

Abstract We present the first statistical census of emission-line variable active galactic nuclei (EVA) at cosmic noon by combining untargeted and deep Hobby–Eberly Telescope Dark Energy Experiment (HETDEX) spectroscopy with multiepoch spectra from the Sloan Digital Sky Survey, DESI, and LAMOST. Anchoring all candidates to a HETDEX spectroscopic epoch and requiring an active galactic nucleus (AGN) classification in either the HETDEX or external epoch(s), we identify a homogeneous sample of 100 EVA at z ∼ 1.5, including 98 that are newly identified. Emission-line variability is selected primarily through statistically significant line-flux changes, supplemented by extensive visual inspections using contemporaneous photometric light curves. The resulting incidence fraction is f EVA ∼ 0.9%. The rest-frame intervals between spectroscopic epochs span ∼1–10 yr, with brightening and dimming events exhibiting statistically indistinguishable characteristic timescales (Δ T ∼ 2.2 and ∼2.6 yr, respectively). A key result is the characterization of the Baldwin effect in the time domain: while many EVA follow the ensemble Baldwin effect (eBeff) between two epochs, a substantial fraction exhibit apparent anti-eBeff responses. Time-resolved spectroscopy of an individual source reveals that the intrinsic equivalent width–luminosity relation is nonstationary, with the line-to-continuum responsivity systematically evolving from stronger to weaker across successive variability cycles; sparse two-epoch sampling of this evolving intrinsic Baldwin evolution naturally produces both eBeff-like and anti-eBeff behaviors. Finally, EVA show no strong preference for extreme Eddington ratios but exhibit a mild tendency toward lower λ Edd values relative to matched control samples, driven primarily by sources observed in their dim states. Together, these results establish a coherent framework for interpreting emission-line variability in AGN at the peak epoch of cosmic black hole growth.

Integrating in-situ modal testing and numerical modelling based on experimental evidence is a powerful strategy for studying the dynamic behavior of existing constructions and making decisions about their retrofitting or monitoring. This paper applies this strategy to analyze the seismic behavior of an unreinforced masonry arch bridge in Spain, investigating on two different approaches to account for the backfill material. An extensive experimental campaign was conducted to determine the bridge’s mechanical and dynamic properties. Operational modal analysis, carried out using two accelerometer setups, was used to extract frequencies and modal shapes through algorithms in both the time and frequency domains. Two finite-element models of the bridge were built and identified: one that treats the backfill between the masonry wall faces as a structural material and another that does not include the backfill, instead redistributing its mass within the masonry density. The two models’ modal behavior and dynamic responses to a real earthquake were then compared to evaluate the effect of different ways of accounting for backfill.

_ In the manufacturing industry, the problem of circle plate packing processing is common and important. In this study, an innovative algorithm is proposed to transform the packing of a circle in a rectangular container into the motion of a disturbed object group. First, the discrete element method (DEM) is used to construct the mathematical model of equal circle motion. By establishing the time domain and complex frequency domain models, the internal stability of the motion system and the feasibility of the algorithm implementation are confirmed. Second, the sweep and prune method and the quadtree method are introduced for collision detection, and the continuous collision detection is used to deal with the tunneling phenomenon caused by high-speed motion. Then, based on the impulse method, the collision circle is processed, and the impulse size is accurately calculated from the two aspects of velocity change and position constraint. The error compensation is introduced to reduce the calculation error, and the calculated impulse updates the circular velocity and position in real time. Finally, the feasibility of the algorithm is verified by an example test, which provides a new direction for solving the circle packing problem. Keywords two-dimensional packing, equal circle packing, stability analysis, collision detection, collision response

Typhoon-induced wind loads pose severe threats to transmission systems. However, existing resilience assessment approaches typically rely on sparse meteorological station data and assume spatially uniform wind speed distributions along transmission corridors, which fail to capture the span-level spatial difference of wind fields. To address this limitation, this paper proposes a distributed optical fiber sensing (DOFS)-driven span-level resilience assessment and hardening optimization framework for transmission networks. First, a phase-sensitive optical time domain reflectometry (Φ-OTDR)-based distributed optical fiber sensing system is employed, utilizing optical fibers embedded in existing OPGW cables as sensing media. By capturing vibration responses of the fiber induced by wind–structure interaction, real-time spatiotemporal wind speed sequences at the individual span level are reconstructed through signal processing and inversion algorithms, providing high-spatial-resolution environmental input data for resilience evaluation. Second, a span-level failure probability quantification method is established using a load–strength interference model. On this basis, a resilience evaluation framework—“span-level asset damage cost—line-level critical corridor identification—system-level load shedding assessment”—is constructed, enabling cross-scale resilience quantification from component damage to system-level performance degradation. Third, a span-level gradient hardening optimization model is developed. By adopting a scenario pre-calculation and iterative updating strategy, coordinated solving of reinforcement decisions and failure scenarios is achieved, thereby maximizing resilience enhancement benefits. The proposed framework is validated using DOFS-measured wind speed data collected from a 500 kV transmission line along the Fujian coast during three real typhoon events—Typhoon Shantuo, Typhoon Trami, and Typhoon Koinu—supporting the reliability of the acquired span-level wind speed information. Case studies conducted on a modified IEEE RTS-24 system demonstrate that the proposed span-level hardening strategy can substantially reduce reinforcement cost compared with the conventional line-level hardening strategy. In the reported benchmark case, it achieves zero load-shedding penalty with a markedly lower hardening cost, and under the same budget constraint, it further yields lower expected load shedding and lower expected asset damage.