Search Results (527)

Visual perception is an important component of close-range maritime situational awareness, particularly when conventional sources such as AIS and radar are delayed, incomplete, or unavailable. Ship wakes provide motion-related visual cues, but their segmentation remains difficult because wake regions are elongated, weakly textured, and frequently mixed with water-surface clutter. This study develops Shipwake-YOLO, a wake-oriented adaptation of YOLOv9-Seg for ship and wake instance segmentation in inland-waterway images. The task is formulated as visual navigational-state cue extraction rather than validated future manoeuvre prediction. The model segments hull and wake instances and provides mask-derived spatial cues for possible downstream state interpretation. The architecture introduces iAFF into cross-scale feature fusion, adapts the high-level SPPELAN aggregation block with SAConv-enhanced convolution, replaces selected downsampling paths with iSACADown, and adopts MPD-IoU as the bounding-box regression loss. On a 2100-image dataset collected from the Wuhu Channel of the Yangtze River, Shipwake-YOLO improves Box-mAP@50 from 77.7% to 84.6% and Mask-mAP@50 from 67.6% to 79.8% relative to the YOLOv9-Seg baseline. Under stricter IoU thresholds, the model reaches 48.9 in Box-mAP@[0.50:0.95] and 46.2 in Mask-mAP@[0.50:0.95]. The parameter count is reduced by 7.5%, and GFLOPs decrease from 144.2 to 137.1. These results indicate that the proposed adaptation improves ship-wake perception within the collected inland-waterway setting and provides a visual basis for downstream navigational-state estimation. Full article

Machine learning prediction of turbulent bursting in near- and far-wake flow zones past two horizontal cylinders was studied in the present article. Based on the bursting dataset, two predictive models were constructed using Artificial Neural Networks (ANNs) and Support Vector Regression (SVR) with stress ratios as target values for each bursting event. After analyzing a number of plots, it was observed that the ANN and SVR models achieved satisfactory estimation accuracy, with minor overfitting specifically in the case of ANN models. By using deep learning for quadrant analysis and highlighting the adaptability of machine learning methods in open-channel turbulence, the current work should strengthen the understanding of bursting occurrences in bluff-body hydrodynamics. Full article

As core structural components for deep-sea oil and gas exploitation, deep-sea risers are continuously subjected to wind, wave, and current loads, which readily induce vortex-induced vibration (VIV) and further trigger structural fatigue damage. Furthermore, the progressive exploitation of deepwater and ultra-deepwater oil and gas resources has exacerbated the complexity and risk of riser VIV, rendering it a critical engineering problem that urgently requires effective solutions. This paper presents a comprehensive review of numerical studies on deep-sea riser VIV, systematically elaborating the fundamental principles, research advances, and application scenarios of three mainstream numerical approaches: semi-empirical models, computational fluid dynamics (CFD) models, and computational structural dynamics (CSD) models. The respective accuracy advantages and inherent limitations of each numerical method are thoroughly analyzed. Additionally, this review focuses on key research hotspots and challenging issues, including VIV responses of flexible risers, dynamic fluid–structure boundary coupling, internal–external flow coupling effects, wake interference of multi-riser systems, efficient VIV prediction, and vibration suppression optimization. The current technical bottlenecks in existing research are clarified. This study aims to provide a systematic theoretical framework and methodological reference for subsequent numerical investigations and engineering applications of riser VIV, and offer technical support for the optimal structural design and safety risk prevention of deep-sea riser systems. Full article

This paper examines how inflow-state discretisation affects wake-loss and annual energy production (AEP) estimates for offshore wind farms. A reproducible workflow is presented for constructing weighted inflow-state ensembles from long-term offshore wind datasets using empirical wind-speed–direction occurrence frequencies. Hub-height wind speeds are reconstructed from multi-level wind data using a time-varying power–law shear exponent, after which the wind climatology is discretised using configurable directional sectors and wind-speed bins. The methodology was evaluated using both a controlled synthetic wind dataset and offshore climatological datasets processed through the same inflow-state and wake-modelling workflow. The analysis quantified how directional resolution, wind-speed bin width, and sector-mean inflow representations affect predicted turbine power, wake loss, and AEP relative to empirical reference cases. For the synthetic dataset, replacing the within-sector wind-speed distribution with a single sector-mean wind speed produced an annual power difference of 12.58%, with seasonal differences ranging from 6.66% in JJA to 13.91% in DJF. Offshore wake-model calculations showed the same overall behaviour. Reducing the empirical inflow-state ensemble from 1593 to 416 retained states changed annual AEP by only 0.03% and wake loss by $-0.03$ percentage points, whereas the sector-mean inflow representation increased predicted AEP by 18.40% and wake loss by 5.13 percentage points relative to the empirical reference case. The results show that preserving the within-sector wind-speed distribution has a larger influence on predicted wake loss and AEP than moderate reductions in retained state count or directional resolution for the datasets and layouts considered here. Empirical inflow-state ensembles using 36 directional sectors together with 1 $\mathrm{m}$${\mathrm{s}}^{-1}$ or 2 $\mathrm{m}$${\mathrm{s}}^{-1}$ wind-speed bins remained within 0.03% of the higher-resolution annual AEP reference while reducing the number of retained inflow states by approximately 74%, with a corresponding reduction in the number of wake-model evaluations required. Full article

The tower with an attached vent stack is a special arrangement in chemical tower structures. Flow-induced vibration of this configuration directly affects the safe operation and structural fatigue life of the equipment. This paper investigates the vortex-induced vibration (VIV) characteristics of a two-cylinder system consisting of a tower and its attached vent stack. Through fluid–structure interaction (FSI) simulations of two unequally sized cylinders in a bundled arrangement, the vibration responses under first and second-mode critical wind speeds with a flow direction of 0° are analyzed. The analysis examines lift and drag coefficients, vibration displacements, and wake flow evolution to reveal the vibration response pattern under multi-parameter coupling. When the lift forces obtained from FSI are applied in a static calculation, the static results for both the first and second-mode critical wind speeds are approximately 250% larger than the FSI results, indicating a significant discrepancy. Further analysis shows that in the FSI simulations, a notable phase difference exists between the fluid excitation and the structural response, causing the lift force to do negative work during part of the vibration cycle, thereby limiting the net energy input. Under the second-mode critical wind speed, the lift distribution along the tower height is significantly non-uniform. The conventional static calculation method neglects both the phase difference and the non-uniform lift distribution along the height, leading to overly conservative predictions. Full article

Next-generation unmanned aerial vehicles require compact propulsion systems capable of providing efficient vertical lift, rapid thrust vectoring, and improved maneuverability. Cyclorotors represent a promising alternative to conventional propellers, but their aerodynamic behavior is governed by highly unsteady blade–wake interactions, making performance prediction challenging. This study investigates a four-bladed cyclorotor equipped with NACA 0012 airfoils using transient computational fluid dynamics simulations and a calibrated semi-analytical blade-element model. The numerical analysis was performed over a rotational-speed range of 368–2305 rpm and for several pitch-amplitude configurations, including 5°, 7.5°, 10°, 12.5° and 15°. The results showed that the favorable pitch amplitude decreases with increasing rotational speed, shifting from larger amplitudes at low RPM to approximately 5° at higher RPM values. The semi-analytical model reproduced the main CFD trends for lift, drag, moment, and power, providing a reduced-order tool for preliminary cyclorotor performance estimation. The comparison confirmed that pitch-amplitude selection strongly influences aerodynamic loading and efficiency and should therefore be adapted to the operating regime. The proposed CFD-based methodology, supported by semi-analytical modelling, provides a useful framework for the aerodynamic characterization and early-stage optimization of cyclorotor propulsion systems for UAV applications. Full article

Background: Continuous Positive Airway Pressure (CPAP) therapy is the standard treatment for obstructive sleep apnea–hypopnea syndrome (OSAHS), and photoplethysmography (PPG) sensors are commonly used in wearable devices for home sleep apnea testing. The recorded airflow and PPG signals from both sensors capture rich physiological patterns. We hypothesize that by combining information from these signals, we can efficiently estimate sleep dynamics of patients receiving CPAP treatment. Methods: The airflow signals were obtained from CPAP titration devices, denoted as CPAP-airflow, while the PPG signals were collected using the PranaQ TipTraQ (TTQ001), a fingertip-worn wearable device. We separately trained one-dimensional convolutional neural networks for CPAP-airflow and PPG signals and fused their outputs through probabilistic ensembling to predict sleep stages. The ensemble method is a late-fusion soft-voting scheme that computes a linearly weighted combination of synchronized softmax probability vectors from the modality-specific models. Results: For three-stage classification (Wake, REM, NREM), the PPG-based and CPAP-airflow-based models achieved overall Cohen’s kappa scores of 0.511 and 0.452, respectively, while the ensembled model improved the overall kappa to 0.587. The F1-score for the REM stage improved to 0.706 using the ensemble method, compared to 0.685 and 0.532 achieved by the individual models, respectively. In the four-stage classification (Wake, REM, Light, Deep) task, a deep sleep sensitivity of 0.596 was attained through the application of probabilistic ensembling. Conclusions: A fusion scheme of complementary information from the CPAP and PPG enhances the accuracy of sleep stage detection and hence enables more precise sleep monitoring, especially with an improved REM identification. Clinical implications include applying the proposed algorithm to improve in-home auto-CPAP titration by capturing REM-related respiratory instability and avoiding under-titration in REM-dominant OSAHS, better reflecting the patient’s true nocturnal respiratory needs. Full article

The global transition to a diversified renewable energy portfolio requires reliable assessment of predictable marine energy resources. This study develops a high-resolution, three-dimensional Regional Ocean Modeling System (ROMS) to quantitatively evaluate theoretical tidal current energy resources in the Bohai and Yellow Seas. The model, configured with fine-scale bathymetry and forced by harmonic tidal constituents, is validated against tide gauge and Acoustic Doppler Current Profiler (ADCP) observations. Multi-year simulations reveal pronounced spatial heterogeneity in tidal current energy distribution. Rather than treating resource assessment as a single power density mapping exercise, this study combines annual mean theoretical power density, peak theoretical power density, threshold-dependent effective flow duration, effective water depth, current directionality, and vertical velocity structure to characterize resource intensity, temporal persistence, and vertical deployability. The results identify distinct hydrodynamic resource regimes. High theoretical resource intensity is concentrated west of Laotieshan Cape and east of Chengshantou, where cumulative annual effective flow duration exceeds 5000 h and short-term instantaneous theoretical power density can reach approximately 10 kW/m² and 8 kW/m², respectively. These peak values indicate strong local tidal acceleration but should be interpreted together with annual mean power density and effective flow duration. In contrast, the northern Jiangsu coastal area exhibits lower peak intensity but relatively persistent moderate flow conditions. The results provide a hydrodynamic resource basis for preliminary site screening and for guiding subsequent turbine-performance, wake/array, environmental, grid accessibility, and techno-economic assessments. Full article

Accurate performance prediction for large offshore wind turbines requires a principled treatment of uncertainty in both the wind resource and the rotor design parameters. In the present work, we develop a surrogate-based, multi-level uncertainty quantification (UQ) framework coupling a physics-based Blade Element Momentum (BEM) solver with a spectral Polynomial Chaos Expansion (PCE) surrogate that replaces the expensive Monte Carlo loop and apply it to the IEA 15 MW offshore reference wind turbine. The framework is completed by Sobol variance-based global sensitivity analysis. The contribution is methodological rather than algorithmic: although each individual ingredient (PCE, Sobol, BEM, and Jensen) is well established, their joint deployment in a single, internally consistent, end-to-end probabilistic workflow that simultaneously delivers (i) aerodynamic–structural UQ with analytical Sobol ranking, (ii) a like-for-like cross-comparison of three reference turbines, (iii) a quantitative leading-edge icing degradation study, and (iv) a farm-level wake-steering optimization on the same IEA 15 MW reference rotor yields a unified probabilistic envelope from which manufacturing tolerances, cold-climate investment thresholds, and farm-layout/control trade-offs can be read off consistently. Five input parameters are treated as random variables: hub-height wind speed (Weibull, k = 2.2, c = 9.8 m/s), air density, blade chord length, twist angle, and rotor speed. A degree-4 sparse PCE is built by non-intrusive spectral projection using N = 5000 Sobol quasi-random realizations, which allows the Sobol indices to be recovered analytically from the expansion coefficients at essentially no extra cost. Three parallel engineering studies complement the core UQ analysis: (A) a head-to-head comparison of the NREL 5 MW, DTU 10 MW, and IEA 15 MW reference turbines; (B) a quantitative assessment of leading-edge ice accretion at four severity levels; and (C) a Jensen-based wake optimization for a 25-turbine offshore array with static wake steering. The main results are as follows: the turbine reaches C_p,max = 0.480 at λ_opt = 8.51, and an annual energy production (AEP) of 71,261 MWh/year (PCE: 70,840 ± 2,140 MWh/year, 95% CI). Wind speed emerges as the dominant driver of C_p variance (S₁ = 0.412), followed by blade twist (0.198) and chord (0.143). Severe icing (30 kg/m) reduces C_p by 18.2% and increases the blade-root Damage Equivalent Load (DEL) by 18.5%. For the array, the optimal spacing (s_x = 8D, s_y = 6D) gives a farm efficiency of 89.6% and 1296 GWh/year, and a 15° wake-steering offset adds a further +3.2% to farm AEP. Compared with plain Monte Carlo, the sparse PCE delivers the same statistics with about 36% fewer model evaluations and a relative error below 0.8%. Full article

Background/Objectives: The Seated Saline Suppression Test (SSST) is widely used for investigation of primary hyperaldosteronism (PA); however, the impact of a one-hour sitting period prior to SSST (as recommended by the Endocrine Society, with a 7.8 ng/dL aldosterone cut-off) on the test outcome remains unclear. Methods: In 82 individuals (23 males) aged 57.3 ± 12.6 years, BMI 27.1 ± 5.1 kg/m², where the majority were diagnosed with adrenal incidentalomas (n = 75), we performed an SSST preceded by a 120 min postural test (first protocol), or immediately after waking (second protocol, a cross-over design). Results: Though aldosterone and renin concentrations before the onset of SSST were higher after the first protocol (p < 0.001), an overall suppression of aldosterone was not different after the first versus the second protocol (6.34 ± 0.68 ng/dL (mean ± SEM) versus 5.74 ± 0.66 ng/dL, p = 0.124). There was no significant difference between the number of individuals with aldosterone > 10 ng/dL, i.e., nine (11%) for the first protocol versus eight (9.75%) for the second protocol, p = 1.00, and for the “grey zone” aldosterone of 7.8–10 ng/mL after SSST (five (6.1%) in each group, p = 1.0). Aldosterone-to-direct-renin ratio (ADR ratio) was higher in those who failed to suppress aldosterone (p < 0.001) but offered a poor prediction of SSST outcome with the lowest ADR ratio of 0.398 ng/dL/µIU/mL in those with aldosterone > 7.8 ng/dL, and the highest ADR ratio of 26.04 ng/dL/µIU/mL for those with aldosterone < 7.8 ng/dL on SSST. Conclusions: Two-hour postural test before SSST does not significantly impact aldosterone suppression after saline infusion, while the ADR ratio does not fully discriminate between those who suppress and those who do not suppress aldosterone secretion on SSST, at least in those with adrenal incidentalomas. Full article

Tidal turbine wake predictions in regional-scale hydrodynamic models typically account for rotor thrust but neglect the drag of support structures. This study introduces a method for representing turbine support structures as permeable drag volumes within TELEMAC-3D and evaluates their influence on wake characteristics. The method is demonstrated for the 1 MW DeepGen-IV turbine deployed at the Fall of Warness test site at the European Marine Energy Centre, Scotland. The tripod foundation, tower, and nacelle are each implemented as momentum source terms alongside an actuator disc rotor in a regional-scale model with mesh resolution down to 1.5 m with 24 sigma layers and output at 60 s intervals (1 s at instrument locations), validated against seabed-mounted ADCP measurements. Including the support structures improves the agreement with measured wake profiles by 6–18% in root-mean-square error at 3.7 rotor diameters downstream and extends the hub-height 5% velocity deficit distance by an average of three rotor diameters (~54 m), with substantial variability across tidal conditions. The tripod and tower drag also extend the velocity deficit into the lower water column, a feature absent from the rotor-only formulation, with potential relevance to near-bed processes such as bed shear stress and sediment transport which are not examined in the present study. The implementation is in principle extendable to other support concepts and multi-device studies, and the results indicate that support structure drag should be considered in regional wake models where wake persistence and downstream interactions are important. Full article

Bedtime procrastination is linked to poor sleep quality, daytime sleepiness, and altered sleep timing. Identifying the factors influencing this behavior is crucial. Among them, problematic smartphone use can delay bedtime. State boredom, a multidimensional concept (high and low arousal, disengagement, inattention, and time perception), triggers problematic smartphone use as a way to cope with boredom, resulting in delayed bedtime and sleep–wake issues. This study aimed to test mediation models where state boredom predicts sleep-related outcomes both directly and indirectly through smartphone addiction and bedtime procrastination. A total of 259 participants (138 women; mean age = 38.44 years) completed an online survey, including the Mini-Sleep Questionnaire, Bedtime Procrastination Scale, Mobile Addiction Scale, Multidimensional State Boredom Scale, and measures of sleep timing on workdays and free days. Results showed significant positive associations among all variables. Mediation analyses revealed that state boredom directly predicted poor sleep quality and daytime sleepiness, and indirectly predicted smartphone addiction and bedtime procrastination. Additionally, boredom indirectly influenced sleep timing via bedtime procrastination. Overall, the findings suggest that boredom can lead to problematic smartphone use, which in turn delays bedtime, resulting in poorer sleep quality, increased daytime sleepiness, and delayed sleep timing. Full article

Background/Objectives: Disorders of consciousness (DOC), including vegetative state/unresponsive wakefulness syndrome (VS/UWS) and minimally conscious state (MCS), present significant diagnostic challenges. Misdiagnosis rates approach 40%, often due to limitations in detecting subtle behavioural responses through clinical observation alone. Eye-tracking technology offers objective, quantitative assessment of visual behaviours that may reveal covert signs of consciousness. This systematic review aimed to evaluate the diagnostic accuracy of eye-tracking technology compared to the Coma Recovery Scale-Revised (CRS-R) for detecting visual responses and consciousness signs in patients with DOC; to examine stimulus effects; and to assess prognostic value. Methods: A systematic literature search was conducted across SciSpace, Google Scholar, PubMed, and institutional libraries following PRISMA 2020 guidelines. Eligibility criteria specified studies involving patients with prolonged DOC assessed using eye-tracking technology. Data extraction, risk of bias assessment, and GRADE certainty evaluation were conducted systematically. Results: Fifteen studies (n = 4–123 patients; published 2012–2025) were included. Eye-tracking detected visual responses in significantly more patients than clinical observation alone (46.2% vs. 18.1% in one study). Mirror stimuli demonstrated the highest detection sensitivity (97% vs. 69% for person and 57% for object). Affectively salient stimuli elicited stronger tracking responses in patients with MCS (37.3% vs. 29.9–30.6% neutral). Advanced VR-based systems achieved high diagnostic accuracy (sensitivity 100%, specificity 88.9%) with prognostic value (overt tracking predicting 62.5% command-following at one year). GRADE certainty was Low for detection rates and diagnostic discrimination, and Very Low for sensitivity, specificity, and prognostic outcomes. Conclusions: Eye-tracking provides objective, sensitive assessment of visual behaviours in patients with DOC and may reduce misdiagnosis rates, supporting a conditional recommendation for its use as a supplementary assessment tool alongside CRS-R. Methodological heterogeneity, small sample sizes, and absence of blinding limit certainty. Adequately powered, multicentre prospective studies are urgently needed. Full article

In high-penetration wind power systems, outage loss uncertainty analysis is fundamental to maintenance scheduling, and its accuracy critically depends on real-time wind power generation, which is dominated by ultra-short-term wind speed fluctuations. Accurate wind speed prediction is therefore essential for reliable outage loss evaluation and subsequent maintenance decision-making. Dense turbine layouts in wind farms lead to strong wake effects, resulting in complex physical attenuation and spatiotemporal correlations in wind speed between upstream and downstream turbines. Leveraging upstream turbine information can therefore enhance the accuracy of downstream wind speed forecasting. However, existing approaches that incorporate neighboring information, such as graph neural networks, rely primarily on data-driven learning and do not explicitly account for the physical mechanisms of wake attenuation, which limits their predictive performance. To address these challenges, a physics-informed ultra-short-term wind speed forecasting method is proposed which integrates an LSTM network for temporal feature extraction with the Jensen wake model through a weighted loss function within a PINN framework. Wake relationships are first identified based on wind direction and turbine layout, and the Jensen wake model is employed to characterize downstream wind speed attenuation. The weighted loss jointly optimizes data-driven and physics-based objectives, enabling the model to coordinate temporal pattern learning with wake-related physical interactions while adhering to wake decay physics. Moreover, the proposed approach accounts for topology-sensitive power flow variations under high-penetration renewable systems, where outage losses are strongly influenced by real-time wind power and wake-effect uncertainties. Case studies demonstrate that, compared with a conventional LSTM model, the proposed method reduces the normalized mean absolute error and the normalized root mean square error by 14.3% and 13.5%, respectively, indicating improved forecasting accuracy and potential for more reliable system outage analysis. Full article

Wireless networks are increasingly considered for industrial and time-critical applications, where flexible deployment must be reconciled with predictable communication behaviour. IEEE 802.11ax introduces mechanisms such as Orthogonal Frequency Division Multiple Access (OFDMA), Trigger-based Uplink Access (TUA), and Target Wake Time (TWT) as part of ongoing efforts to support bounded latency and deterministic transmissions in Wi-Fi networks. However, the practical behaviour of these mechanisms depends not only on the standard, but also on what commercial devices expose, how access points implement scheduling decisions, and how trigger-based access, RU assignment, and timing control can be configured in real deployments. This paper therefore focuses on the practical implementation and experimental assessment of OFDMA-based deterministic operation using Wi-Fi 6 commercial off-the-shelf (COTS) hardware. The proposed configuration combines driver-level enabling of high-efficiency mechanisms with controlled testbed measurements and complementary simulations, allowing OFDMA operation to be compared against conventional single-user OFDM under realistic traffic and interference conditions. The results show that coordinated OFDMA operation on COTS devices improves temporal stability, reducing jitter by up to 23% and latency by approximately 44% with respect to single-user OFDM operation. The experiments also reveal practical effects that are central to deterministic-oriented Wi-Fi: simultaneous RU-based transmissions reduce contention-driven variability, TWT-based activity windows improve temporal alignment, and RU subdivision introduces a throughput trade-off that must be considered when dimensioning industrial traffic. Overall, the study provides empirical evidence that Wi-Fi 6 can support deterministic-oriented industrial communication when OFDMA, trigger-based access, and timing mechanisms are jointly configured, while also highlighting the implementation constraints that remain when moving from standard capabilities to COTS device behaviour. Full article