Search Results (1,782)

Hydraulic engineering structures can threaten freshwater fish by entraining them into hazardous areas. Acoustic barriers have been proposed as a non-physical method to guide fish away from these zones. In this study, we investigated the behavioral responses of juvenile grass carp to different acoustic stimuli under semi-natural conditions using outdoor net cages. Four sound types were tested: a 1000 Hz pure tone and three broadband sounds, including Alligator sinensis hissing, pile-driving noise, and outboard motor noise. Behavioral responses were quantified using response frequency, total midline crossings, first-response time, maximum swimming speed, and average swimming speed. The results showed that Alligator sinensis hissing elicited the highest number of midline crossings, representing the strongest behavioral response among all tested sounds. In addition, both Alligator sinensis hissing and outboard motor noise induced significantly stronger avoidance responses than the pure tone or pile-driving noise, as indicated by higher response frequency and faster swimming speeds. Furthermore, manipulation of pulse repetition intervals in the most effective deterrent sounds generated a novel broadband sound, which altered fish distribution patterns and elicited avoidance behavior. These findings indicate that both sound type and temporal structure influence negative phonotaxis behavior in grass carp and provide experimental evidence for the optimization of acoustic barriers in fish management. Full article

Background/Objectives: The composition of atherosclerotic plaques is increasingly recognized as a key factor determining cardiovascular risk. Features such as intraplaque hemorrhage, a necrotic lipid core, and the integrity of the fibrous cap are strongly associated with plaque instability and the occurrence of adverse clinical events. Magnetic resonance imaging (MRI) allows for non-invasive characterization of plaque microstructure through quantitative mapping of T1 and T2 relaxation times; however, image noise may limit the accuracy of these measurements. Methods: In this experimental study, a total of 15 ex vivo atherosclerotic plaque samples were imaged using a 1.5T scanner with a fast spin-echo sequence featuring variable repetition times (TR: 200–12,000 ms) and echo times (TE: 21–240 ms) to obtain T1 and T2 maps. An Attention–Residual–Dense U-Net neural network was trained on pairs of noisy and reference images to reduce Rician noise while preserving structural details. Results: The 15 samples examined exhibited T1 values ranging from 1768 to 3294 ms and T2 values ranging from 138 to 202 ms, which were shorter than those for water (T1: 3323 ms; T2: 114 ms), which is consistent with the presence of collagen, lipids, and mineral deposits. Variability among samples reflected differences in composition, with the shortest relaxation times suggesting advanced calcifications. The application of deep learning methods allowed for a threefold improvement in the signal-to-noise ratio (SNR) while preserving the microarchitecture of the lamina. Conclusions: Quantitative T1/T2 mapping combined with deep learning-based image enhancement methods constitutes a robust tool for high-resolution characterization of atherosclerotic plaque composition under ex vivo conditions. The results obtained indicate the potential for translating this method to in vivo studies to better detect tissue heterogeneity and features associated with plaque instability. Full article

The increasing deployment of wind energy has brought renewed attention to aeroacoustic noise generated by wind turbine blades, where broadband noise is primarily associated with vortex shedding at the trailing edge (TE) and leading edge (LE) of airfoils. Owls, particularly Tyto alba, exhibit wing morphologies such as serrations, velvet-like surfaces, and fringes that enable silent flight through aerodynamic noise suppression. This study presents a scoping review of the scientific literature on owl-inspired serration strategies applied to aerodynamic airfoils and wind turbine blades. The literature search was conducted across major academic databases, including Scopus, ScienceDirect, SpringerLink, and MDPI, covering publications from 1970 to 2025. A total of 69 experimental and numerical studies focusing on LE and TE serrations was analyzed. The review integrates aeroacoustic analysis with bio-inspired design perspectives. The analyzed studies consistently show that serrated geometries modify vortex dynamics and turbulence structures, leading to measurable acoustic benefits. Experimentally, the largest reductions reported for aerodynamic airfoils reached about 7 dB for both LE and TE serrations, mainly as broadband noise attenuation, in specific frequency ranges. Numerically, the highest reported reduction reached up to 21 dB for a serrated TE configuration, corresponding to spectral SPL reduction mainly below 1.6 kHz. The reviewed studies also indicate that the associated aerodynamic response is strongly configuration-dependent, ranging from limited penalties to measurable changes in lift, drag, power output, or structural loading. Numerical simulations further support experimental findings and highlight the importance of geometric parameters such as serration amplitude, wavelength, and spacing. Overall, bio-inspired serrations represent a promising passive strategy for aeroacoustic noise mitigation in wind turbines, drones, and rotating aerodynamic systems. Future research should focus on the multi-objective optimization of serration geometry, large-scale experimental validation, and the integration of bio-inspired concepts into industrial blade designs. Full article

Planning effective collision avoidance routes is a crucial measure for ensuring ship safety. However, position uncertainty caused by sensor noise, communication delays, and sudden changes in the maneuvering of target vessels severely restricts the reliability of traditional collision avoidance methods. To address this, this study integrates the velocity obstacle method and conditional value at risk theory to design a ship collision avoidance framework under position uncertainty. The position uncertainty of the target vessel is modeled using a Gaussian distribution. By fusing multi-source sensor data from radars and the Automatic Identification System through Bayesian inference, the posterior estimate of the vessel’s position is dynamically updated, thereby constructing an uncertainty velocity obstacle region. The Gaussian posterior distribution of the position is incorporated into a stochastic loss function to formulate a stochastic optimization model that balances navigation efficiency and collision risk. The model is solved using the sample mean approximation method and strictly complies with the International Regulations for Preventing Collisions at Sea. The results of two sets of multi-vessel encounter simulations demonstrate that, compared with traditional methods, the proposed method achieves superior performance in terms of total path length and algorithm runtime. It is capable of generating compliant collision avoidance strategies in complex dynamic crossing scenarios, attaining optimal comprehensive performance with respect to safety, economy, and regulatory compliance. Full article

Faults in aero-engine rotating components account for more than 60% of total failures, and their early features are easily masked by noise under complex conditions. Traditional single-sensor diagnosis suffers from low feature utilization, poor interpretability, and weak cross-condition generalization. This paper proposes a multi-source fault diagnosis method for aero-engines based on an explainable boosted tree, integrating spatiotemporal attention (STA) and adaptive feature selection (AFS). We collect multi-domain data from four standard core sensors widely used in existing engine health management systems and extract multi-dimensional features to build a heterogeneous feature set. Adaptive feature selection is implemented using mutual information and a variance inflation factor. A spatiotemporal attention mechanism is introduced to weight and fuse features effectively. The fused features are used to train an XGBoost classifier, and SHAP values are adopted to quantify feature contributions and improve model interpretability. Uncertainty sources and sensitivity boundaries are quantitatively analyzed to support engineering acceptance. The method achieves high sensitivity to early weak faults and stable uncertainty under complex operating conditions. Tests on a fault simulation test rig show that the proposed method achieves 99.2% diagnosis accuracy and 97.5% cross-condition generalization accuracy, outperforming conventional models. It can identify early weak fault signatures, clarify key fault indicators, and provide a quantitative basis for fault tracing and maintenance decision-making. The method employs a standard sensor suite without additional hardware costs, features lightweight computation and low inference overhead, and delivers clear economic benefits by reducing false alarms, avoiding unplanned downtime, and optimizing maintenance resources. It offers a reliable, cost-effective solution for aero-engine fault diagnosis under complex operating conditions. Full article

In this paper, we present a study on the automated design optimization of a wideband low-noise amplifier (LNA) operating in Ku-band (12 to 18 GHz) using proximal policy optimization (PPO), one of the widely applied reinforcement learning (RL) algorithms for engineering problems. As a target microwave active circuit, we select a two-stage LNA architecture, where transmission lines (TLs) are dominantly used for impedance matching and gain/noise optimization. For simplicity, all widths of TLs were fixed so that the characteristic impedance is 50 Ω, with lengths of TLs being set as design parameters. In addition, dimension variables of capacitors were treated as design parameters and, in total, we optimized 29 parameters. For target specifications, we set both $S_{11}$ and $S_{22}$ to be below −10 dB over the 12–18 GHz band and the noise figure (NF) to be below 2 dB. A total of 20,140 simulations were performed for training and the overall process took about 24 h. The results show that both the reward and the loss converged appropriately, achieving the target specifications successfully. For the final results, we performed up to 25 predictions, and the prediction process was terminated early if a solution meeting all target specifications was found within the given number of attempts. The device model used was a commercial 150 nm GaN high-electron-mobility transistor (HEMT) process technology. Full article

The global transition toward carbon-neutral energy solutions has established Solid Oxide Fuel Cells (SOFCs) as a key technology for next-generation power generation. This work presents a comprehensive numerical study and multi-factor statistical analysis of an anode-supported SOFC fueled by synthetic diesel. A three-dimensional computational fluid dynamics model, validated against experimental data, was integrated with a Taguchi L₂₇ orthogonal array to systematically evaluate the influence of six key parameters: temperature, fuel mass flow rate, operating pressure, current load, flow channel configuration, and methane molar fraction. Statistical analysis through the signal-to-noise ratio and analysis of variance identified the operating current as the most significant factor affecting cell voltage, followed by the fuel mass flow rate and temperature. The experiments showed that the highest levels of all factors (except for the current, which had the lowest level) maximize electrochemical performance while maintaining a steam-to-carbon ratio (S/C) within a range of 0.83 to 0.92, calculated based on total carbon content, ensuring sufficient humidification for internal reforming across all tested fuel compositions. Furthermore, a multiple linear regression model was developed as a computationally efficient surrogate, demonstrating exceptional predictive accuracy with an R² of 0.9954 and a mean relative error of 1.76% across independent validation cases. These results provide a robust methodology for rapid design and sensitivity analysis of internal-reforming SOFCs, offering a precise tool for optimizing fuel utilization in high-temperature electrochemical systems. Full article

Environmental noise is increasingly recognized as a major environmental and public health challenge, with road traffic identified as the dominant source of acoustic pollution across Europe. In this context, noise mitigation is directly linked to sustainable development goals related to human health and urban sustainability. Noise barriers are among the most widely implemented mitigation strategies; however, their spatial distribution and adequacy remain poorly documented, limiting their effectiveness for sustainable territorial planning. This study develops the first georeferenced database of highway noise barriers in Andalusia (Spain) and applies a reproducible, transdisciplinary geospatial workflow integrating field surveys, remote-sensing tools, and Geographic Information Systems (GIS). A total of 110 barriers were mapped, classified by material, geometry, and surrounding land use, and analyzed in relation to sensitive receptors, including dwellings, schools, and hospitals. Results show that only 1.6% of the Andalusian highway network is currently protected by noise barriers, with strong territorial disparities: over 50% of all structures are concentrated along coastal metropolitan corridors, while extensive inland areas remain unprotected. Misalignments were also detected between barrier placement and officially reported high-exposure segments, indicating limited correspondence between infrastructural deployment and planning-designated priority areas. Beyond generating a comprehensive regional dataset, the proposed methodology provides a scalable basis for national and European initiatives seeking to harmonize the mapping and assessment of noise-mitigation infrastructures. By offering an open-access, transferable framework, this work contributes to a more equitable distribution of environmental protection measures and supports policy professionals, environmental managers, and planners in advancing healthier and more sustainable urban and transport systems. Full article

Underwater gated time-correlated single-photon-counting (TCSPC) LiDAR is advantageous when weak target echoes coexist with strong backscatter. However, under the first-photon-triggering and SPAD dead-time mechanism, the estimated time of flight becomes dependent on the return strength, thereby producing a range walk error (RWE). This paper develops a condition-calibrated correction framework for accumulated-histogram underwater ranging in the low-photon regime. A non-homogeneous Poisson first-arrival model that jointly includes gate-limited signal photons and in-gate background triggering yields a computable expression for the total trigger probability and the conditional first-arrival time. A first-order expansion around $N_{pe}\approx 0$ leads to an approximately linear RWE–$N_{pe}$ relation under the present system–water condition. A density-based signal-window localization method and a noise-occlusion-compensated estimator of $N_{pe}$ are combined with reference-plane differential calibration. Experiments in a 10 m clear-freshwater tank at 9.11 m show that the mean absolute error is reduced from 39.205 mm to 2.130 mm, corresponding to a 94.57% improvement. Compared with a quadratic model used under higher-photon conditions, the proposed linear model yields an order-of-magnitude smaller residual error in the low-photon region ($N_{pe}<1.6$). Full article

Monitoring surface deformation at reclaimed airports under construction is crucial for ensuring construction safety. However, significant variations in surface scattering characteristics cause severe decorrelation, limiting the effectiveness of conventional single-polarization Interferometric Synthetic Aperture Radar (InSAR). To address the issue of insufficient coherent pixels, we propose a dual-polarization sequential InSAR technique and compare its performance with traditional Persistent Scatterer Interferometry (PSI) and Distributed Scatterer Interferometry (DSI) at the Dalian Jinzhou Bay International Airport (DJBIA). Using 89 Sentinel-1A dual-polarization (VV-VH) images (August 2022 to October 2025), the results demonstrate that VV and VH polarizations exhibit significant spatial complementarity, highlighting the necessity of multi-polarization data. Further, to address the issue of long-term changes in scattering characteristics, we applied the Sequential Estimation and Total Power-Enhanced Expectation Maximization Inversion (SETP-EMI) method, which dynamically integrates dual-polarization information and performs adaptive phase optimization. This approach significantly enhances monitoring capability in low-coherence areas of the airport under construction, effectively suppressing phase noise, improving interferogram quality, and yielding a more complete and reliable deformation field. Overall, this study systematically validates the SETP-EMI method with dual-polarization information for deformation monitoring at reclaimed airports under construction, providing technical support for engineering safety control and research on reclamation subsidence mechanisms. Full article

Bird strike events on rotating jet engine fan blades pose significant risks to aviation safety, yet high-fidelity numerical simulations remain computationally expensive, limiting their use in parametric design studies. This study develops a physics-guided machine learning surrogate framework for predicting bird strike response on rotating Ti-6Al-4V fan blades, systematically comparing Lagrangian (gelatin-based, Mooney–Rivlin) and Smoothed Particle Hydrodynamics (SPH, water-like) formulations. A total of 100 explicit dynamic simulations were conducted in ANSYS LS-DYNA (R2) (50 per formulation), varying bird impact velocity and blade angular speed. Random Forest, Support Vector Regression, Polynomial Regression, and XGBoost regression models were trained and evaluated using five-fold cross-validation. Results demonstrate that SPH-based surrogates achieve superior predictive accuracy, with Random Forest yielding R² = 0.9938 for maximum deformation and R² = 0.9962 for total energy dissipation. In contrast, Lagrangian-based stress surrogates exhibited severe performance degradation (R² = 0.24) due to mesh-dependent numerical noise. The trained surrogates achieved computational speed-up factors of 10⁴–10⁵ relative to direct simulation. These findings establish that surrogate model reliability is fundamentally governed by the numerical quality of the training data, providing guidance for integrating machine learning with impact simulation workflows in aero-engine blade design. Full article

The Variable Cycle Engine (VCE) is a key enabling technology for addressing the economic and environmental challenges of next-generation supersonic civil aircraft. This paper presents a multidisciplinary design analysis and optimization (MDAO) approach to quantitatively assess the potential benefits of Variable Cycle Engines (VCE) in the conceptual design of supersonic civil aircraft. In this approach, component-level models of a conventional Mixed-Flow Turbofan (MFTF) and a double-bypass VCE with a Core Driven Fan Stage (CDFS) are integrated into the MDAO process. Employing a multi-point optimization strategy, the engine design parameters and off-design control schedules are first determined. Subsequently, for each given engine design (MFTF and CDFS VCE), the airframe geometry parameters are optimized to minimize the aircraft Maximum Take-off Weight (MTOW). The application of this approach is illustrated through a case study of a medium-sized supersonic civil transport. The results indicate that, under the assumption of identical weights for the VCE and the MFTF, the design with the VCE reduces the MTOW by 2.8%, block fuel consumption by 5.7%, and total mission Nitrogen Oxides (NOx) emissions by 24.2% compared to the design with the MFTF. Additionally, lateral noise and flyover noise during the take-off phase are decreased by 2.2 EPNdB and 1.9 EPNdB, respectively. To account for the potential weight increase caused by the structural complexity of the VCE, a parametric weight sensitivity analysis is conducted. Results show that the VCE retains its advantages in MTOW, fuel efficiency, noise, and emissions for weight penalty factors up to 1.15. Full article

Due to their low noise and high efficiency, ducted fans are extensively used in unmanned aerial vehicles (UAVs). As the core lift and propulsion units, the aerodynamic performance of dual-ducted fans critically determines propulsion efficiency and flight stability. However, when operating near the ground, variations in ground clearance and the gap between ducts disrupt the isolated flow fields, introducing ground effect and aerodynamic coupling that pose significant stability risks. To address this, we developed a high-fidelity numerical model using the Unsteady Reynolds-Averaged Navier–Stokes approach with sliding mesh technology and the Shear-Stress Transport k-$\omega$ turbulence model. This study reveals the macroscopic aerodynamic characteristics of dual-ducted fans as functions of ground clearance and duct gap, and clarifies the underlying flow mechanisms. The research results indicate that the performance of a signle-ducted fan is highly sensitive to ground clearance: a critical threshold of thrust occurs when the ground clearance (h) at the duct outlet is 0.75 times the rotor disk diameter (D) . Under ground-effect-free conditions, the dual duct gap dominates the aerodynamic interference pattern: the total thrust of the system reaches its maximum value when the minimum spacing between the outer edges of the two ducts is 6 times the rotor disk radius. The coupling effect of ground clearance and duct gap exhibits significant nonlinear characteristics: thrust first decreases and then increases with increasing ground clearance, and the sensitive range of gap variation is $h/D=0.5$–$1.0$. These findings are crucial for optimizing the layout of ducted UAVs and enhancing UAV flight control to ensure safe and efficient operation under near-ground conditions. Full article

This paper presents a nanowatt-scale operational transconductance amplifier (OTA) and an electronically tunable third-order low-pass filter (LPF) designed for energy-constrained biomedical signal conditioning. The circuits are implemented in a 65 nm CMOS process and verified through comprehensive schematic-level simulations. Biased in the deep subthreshold region at 1 nA, the OTA achieves a 50 dB low-frequency gain, a 225 Hz unity-gain bandwidth at 10 pF load capacitance and an input-referred noise floor of 1.55 μV/√Hz, with a total power consumption of only 1.75 nW. The integrated third-order LPF provides a wide tuning range (37–668 Hz) via bias current modulation, exhibiting excellent linearity with a THD of 0.059% and a 65.3 dB dynamic range. Monte Carlo and PVT corner analyses demonstrate the design’s theoretical robustness against process variations and environmental fluctuations. ECG signal simulations validate the circuit’s effectiveness in suppressing high-frequency artifacts while preserving morphological integrity, providing a proof-of-concept for ultra-low-power wearable healthcare architectures. Full article

To address peak edge operation and excessive valve switching in hydraulically coupled multi-zone campus irrigation, this study proposes a collaborative scheduling framework that combines short-term evapotranspiration (ET) forecasting with safety-constrained reinforcement learning. Temperature, relative humidity, and light intensity are used to construct vapor pressure deficit and radiation proxy features, and a lightweight predictor provides two-hour-ahead ET statistics as forward-looking disturbance information. A safety layer composed of Top-2 gating and total flow projection is then used to map policy outputs into a feasible action space under parallel irrigation and total flow constraints. Using seven consecutive days of field data from October 2025, the proposed method reduced total water consumption to 131.04 m³, corresponding to reductions of 9.13% and 6.12% relative to fixed-schedule and hysteresis threshold rotational irrigation, respectively. It also reduced the maximum total flow from 2.00 to 1.60 L/s, lowered valve switching cycles to 12, and reduced the border ratios at 0.90 and 0.95 to 0. Additional ablation, sensing noise/packet loss, and Top-K extension experiments further showed that ET forecasting improves anticipatory scheduling, whereas safety projection is essential for zero-violation operation. These results demonstrate that the proposed framework provides a practical and deployable solution for safe and water-efficient multi-zone irrigation scheduling under shared pump constraints. Full article