Search Results (730)

To address frost heave in winter-lined canals and sediment accumulation in the Hetao Irrigation District of Inner Mongolia Autonomous Region, while reducing long-term maintenance costs of canal linings and relocating sediment as solid waste, this study proposes the use of low-toxicity, environmentally friendly octadecyltrichlorosilane (OTS) to modify channel sediment. This approach aims to improve the frost heave resistance of canal sediment and investigate optimal modification conditions and their impact on frost heave phenomena, aligning with sustainable development goals of low energy consumption and economic efficiency. Water Droplet Penetration Time (WDPT) tests and unidirectional freezing experiments were conducted to analyze frost heave magnitude, temperature distribution, and moisture variation in modified sediment. A coupled thermal–hydraulic–mechanical (THM) model established using COMSOL Multiphysics 6.2 software was employed for numerical simulations. Experimental results demonstrate that the hydrophobicity of channel sediment increases with higher OTS concentrations. The optimal modification effect is achieved at 50 °C with a silane-to-sediment mass ratio of 0.001, aligning with the economic efficiency of sustainable development. The unidirectional freezing test results indicate that compared to the 0% modified sediment content, the 40% modified sediment proportion reduces frost heave magnitude by 71.3% and decreases water accumulation at the freezing front by 21.1%. The comparison between numerical simulation results and experimental data demonstrates that the model can accurately simulate the frost heave behavior of modified sediment, with the error margin maintained within 15%. In conclusion, OTS-modified channel sediment demonstrates significant advantages in enhancing frost heave resistance while aligning with the economic and environmental sustainability requirements. Furthermore, the coupled thermal–hydraulic–mechanical (THM) model provides a reliable tool to guide sustainable infrastructure development for hydraulic engineering in the cold and arid regions of Inner Mongolia, effectively reducing long-term maintenance energy consumption. Full article

Arctic sea ice can be regarded as a sensitive indicator of climate change, and it has declined dramatically in recent decades. The swift decline in Arctic sea ice coverage leads to an expansion of the marginal ice zone (MIZ). In this study, an ice-based buoy with an imaging system is designed for the long-term observation of the changes in sea ice from the packed ice zone to the marginal ice zone in polar regions. The system composition, main buoy, image system, and buoy load were analyzed. An underwater camera supports a 640 × 480 resolution image acquisition, RS485 communication, stable operation at –40 °C, and long-term underwater sealing protection through a titanium alloy housing. During a continuous three-month field deployment in the Arctic, the system successfully captured images of ice-bottom morphology and biological attachment, demonstrating imaging reliability and operational stability under extreme conditions. In addition, the buoy employed a battery state estimation method based on the Extreme Learning Machine (ELM). Compared with LSTM, BP, BiLSTM, SAELSTM, and RF models, the ELM achieved a test set performance of RMSE = 0.05 and MAE = 0.187, significantly outperforming the alternatives and thereby improving energy management and the reliability of long-term autonomous operation. Laboratory flume tests further verified the power generation performance of the wave energy-assisted supply system. However, due to the limited duration of Arctic deployment, full year-round performance has not yet been validated, and the imaging resolution remains insufficient for biological classification. The results indicate that the buoy demonstrates strong innovation and application potential for long-term polar observations, while further improvements are needed through extended deployments and enhanced imaging capability. Full article

Robots with parallelogram mechanisms are widely employed in industrial applications due to their mechanical rigidity and precise motion control. However, the analytical definition of feasible workspace regions free from self-collisions remains an open challenge, especially considering the nonlinear and composite nature of such regions. This work introduces a mathematical model grounded in a collision theorem that formalizes boundary functions based on joint variables and geometric constraints. These functions explicitly define the envelope of safe configurations by evaluating relative positions between critical structural components. Using the MinervaBotV3 as a case study, the symbolic joint-space boundaries and their corresponding geometric regions in both 2D and 3D are computed and visualized. The feasible region is refined through centroid-based scaling to introduce safety margins and avoid singularities. The results show that this framework enables analytically continuous workspace representations, improving trajectory planning and reliability in constrained environments. Future work will extend this method to spatial mechanisms and real-time implementations in hybrid robotic systems. Full article

During the continuous operation of trailing suction hopper dredger (TSHD), equipment workload exhibits significant time-varying characteristics. Maintaining dynamic symmetry between power generation and consumption is crucial for ensuring system stability and preventing power supply failures. Key challenges lie in dynamic perception, accurate prediction, and real-time power management to achieve this equilibrium. To address this issue, this paper proposes and constructs a “prediction-driven dynamic power management method.” Firstly, to model the complex temporal dependencies of the workload sequence, we introduce and improve a dilated convolutional long short-term memory network (Dilated-LSTM) to build a workload prediction model with strong long-term dependency awareness. This model significantly improves the accuracy of workload trend prediction. Based on the accurate prediction results, a dynamic power management strategy is developed: when the predicted total power consumption is about to exceed a preset margin threshold, the Power Management System (PMS) automatically triggers power reduction operations for adjusfigure loads, aiming to maintain grid balance without interrupting critical loads. If the power that the generator can produce is still less than the required power after the power is reduced, and there is still a risk of supply-demand imbalance, the system uses an Improved Grey Wolf Optimization (IGWO) algorithm to automatically disconnect some non-critical loads, achieving real-time dynamic symmetry matching of generation capacity and load demand. Experimental results show that this mechanism effectively prevents generator overloads or ship-wide power failures, significantly improving system stability and the reliability of power supply to critical loads. The research results provide effective technical support for intelligent energy efficiency management and safe operation of TSHDs and other vessels with complex working conditions. Full article

The refined aerodynamic design optimization of multistage compressors is a typical high-dimensional and expensive optimization problem. This study proposes an integrated surrogate model-assisted evolutionary algorithm combined with a Directly Manipulated Free-Form Deformation (DFFD)-based parametric dimensionality reduction method, establishing a high-precision and efficient global parallel aerodynamic optimization platform for multistage axial compressors. The DFFD method achieves a balance between flexibility and low-dimensional characteristics by directly controlling the surface points of blades, which demonstrates a particular suitability for the aerodynamic design optimization of multistage axial compressors. The integrated surrogate model enhances prediction accuracy by simultaneously identifying optimal solutions and the most uncertain solutions, effectively addressing highly nonlinear design space challenges. A three-stage axial compressor in a heavy-duty gas turbine is selected as the optimization object. The results demonstrate that the optimization task takes less than 48 h and achieves an improvement of 0.6% and 4% in the adiabatic efficiency and surge margin, respectively, while maintaining a nearly unchanged flow rate and pressure ratio at the design point. The proposed approach provides an efficient and reliable solution for complex aerodynamic optimization problems. Full article

Tethered unmanned aerial vehicles (UAVs) operating in terrestrial environments face critical safety challenges from power cable breaks, yet existing solutions—including fiber optic sensing (cost > USD 20,000) and impedance analysis (35% payload increase)—suffer from high cost or heavy weight. This study proposes a dual innovation: a real-time break detection method and a low-cost multi-core parallel sensing system design based on ACS712 Hall sensors, achieving high detection accuracy (100% with zero false positives in tests). Unlike conventional techniques, the approach leverages current differential (ΔI) monitoring across parallel cores, triggering alarms when ΔI exceeds I_rate/2 (e.g., 0.3 A for 0.6 A rated current), corresponding to a voltage deviation ≥ 110 mV (normal baseline ≤ 3 mV). The core innovation lies in the integrated sensing system design: by optimizing the parallel deployment of ACS712 sensors and LMV324-based differential circuits, the solution reduces hardware cost to USD 3 (99.99% lower than fiber optic systems), payload by 18%, and power consumption by 23% compared to traditional methods. Post-fault cable temperatures remain ≤56 °C, ensuring safety margins. The 4-core architecture enhances mean time between failures (MTBF) by 83% over traditional systems, establishing a new paradigm for low-cost, high-reliability sensing systems in terrestrial tethered UAV cable health monitoring. Preliminary theoretical analysis suggests potential extensibility to underwater scenarios with further environmental hardening. Full article

This study investigated the thermomechanical behaviour of the nozzle of a GTM 400MOD miniature turbojet engine during combustion of aviation kerosene and co-combustion of kerosene with hydrogen. Numerical analysis was based on experiments conducted on a dedicated test rig at engine speeds ranging from 31,630 rpm to 65,830 rpm, providing data on the temperature and dynamic pressure at the nozzle outlet. These data served as input to numerical analyses using the ANSYS Fluent, Steady-State Thermal, and Static Structural modules to evaluate exhaust gas flow, temperature distribution, and stress and strain states. The paper performed a basic analysis with additional simplifications, and an extended analysis that took into account, among other things, thermal radiation in the flow. The results of the basic analysis show that, at comparable thrust levels, co-firing and pure kerosene combustion yield similar nozzle temperature distributions, with maximum wall temperatures ranging from 978 K to 1090 K, which remain below the allowable limit of 1193 K (920 °C). Maximum stresses reached approximately 261 MPa, close to but not exceeding the yield strength of 316 stainless steel. Maximum nozzle deformation did not exceed 0.8 mm. Small dynamic pressure fluctuations were observed; For example, at 31,630 rpm, co-firing increased the maximum dynamic pressure from 1.56 × 104 Pa to 1.63 × 104 Pa, while at 47,110 rpm, it decreased from 4.05 × 104 Pa to 3.89 × 104 Pa. The extended analysis yielded similar values for the nozzle temperature and pressure distributions. Stress and strain increased by more than 76% and 78%, respectively, compared to the baseline analysis. The results confirm that hydrogen co-firing does not significantly alter the nozzle thermomechanical loads, suggesting that this emission-free fuel can be used without negatively impacting the nozzle’s structural integrity under the tested conditions. The methodology, combining targeted experimental measurements with coupled CFD and FEM simulations, provides a reliable framework for assessing material safety margins in alternative fuel applications in small turbojet engines. Full article

Gas hydrate-bearing sediments in marine environments represent both a future energy source and a geohazard risk, prompting increasing international research attention. In the Shenhu area of the South China Sea, a large volume of drilling and laboratory data has been acquired in recent years, yet a comprehensive framework for evaluating the characteristics of key reservoir parameters remains underdeveloped. This study presents a spatially integrated and statistically grounded framework that captures regional-scale heterogeneity using multi-source in situ datasets. It incorporates semi-variogram modeling to assess spatial variability and provides statistical reference values for geological and geotechnical properties across the Shenhu Area. By synthesizing core sampling results, acoustic logging, and triaxial testing data, representative probability distributions and variability scales of hydrate saturation, porosity, permeability, and mechanical strength are derived, which are essential for numerical simulations of gas production and slope stability. Our results support the development of site-specific reservoir models and improve the reliability of early-phase hydrate exploitation assessments. This work facilitates the rapid screening of hydrate reservoirs, contributing to the efficient selection of potential production zones in hydrate-rich continental margins. Full article

High-resistance faults on the DC lines of multi-terminal VSC-HVDC grids lead to insufficient protection reliability, and the introduction of current-limiting strategies alters the system’s intrinsic fault characteristics, degrading protection performance. To address these issues, we propose a DC-line protection scheme that is immune to converter control strategies and highly tolerant to fault resistance. First, based on the grid topology, post-fault current paths are analyzed, and the fault characteristics produced solely by the fault-induced voltage source are identified. A sequential overlapping derivative transformation is then employed to magnify the discrepancy between internal and external faults, forming the core of the fault-identification criterion; the zero-mode component is used for pole selection. Finally, a four-terminal VSC-HVDC model is built in PSCAD/EMTDC version 4.6.2 for validation. Simulation results show that, after applying the current-limiting strategy, the characteristic quantity changes only marginally, and the proposed protection can reliably withstand fault resistances of up to 700 Ω. Full article

Background/Objectives: Static image captures (SICs) are the prevailing methodology for documenting thyroid nodules (TNs) on ultrasound examinations. Ultrasound cine-loop (CL) video sequences of the thyroid enable the storage and review of the entire organ in PACS, analogous to sectional imaging modalities such as CT or MRI. Expanding on SIC, the collection of more extensive datasets is possible, with the potential to enhance diagnostic performance. However, there is an absence of reliable data concerning this process. Methods: This retrospective, tertiary care, single-center study included all patients with cytologically and histopathologically diagnosed TNs from 01/16 to 12/23. A standardized CL protocol was routinely acquired in addition to conventional SIC. The diagnostic performance of ACR and Kwak TIRADS was examined for both CL and SIC in a PACS. Advantages, challenges, and limitations of CL were analyzed. Conclusions: In total, 189 patients with 329 TNs (78% females, aged 54 ± 15 years; 76% diagnosed via surgery; 14% malignant) were included. On SIC, 58 TNs (18%) were not identified (all benign). When comparing CL with SIC, a strong correlation was observed for nearly all ultrasound features (echogenicity, composition, margin, and foci; each p < 0.001) and both TIRADSs (each p < 0.001). The diagnostic accuracy of CL was slightly superior, with maximum values of 85% for ACR and 87% for Kwak TIRADS, respectively. Rating confidence and image quality exhibited superiority on SIC (each p < 0.001). The occurrence of image artifacts was more prevalent in CL (p < 0.001). The integration of cine loops into thyroid ultrasound was found to be a seamless process, thereby enhancing the risk stratification of nodules. Image quality impairments manifested more frequently in cine loops, while static image captures demonstrated higher levels of assessment confidence. Full article

DIALOGUE (DIagnostic AI Learning through Objective Guided User Experience) is a generative artificial intelligence (GenAI)-based training program designed to enhance diagnostic communication skills in medical students. In this single-arm pre–post study, we evaluated whether DIALOGUE could improve students’ ability to disclose a type 2 diabetes mellitus (T2DM) diagnosis with clarity, structure, and empathy. Thirty clinical-phase students completed two pre-test virtual encounters with an AI-simulated patient (ChatGPT, GPT-4o), scored by blinded raters using an eight-domain rubric. Participants then engaged in ten asynchronous GenAI scenarios with automated natural-language feedback. Seven days later, they completed two post-test consultations with human standardized patients, again evaluated with the same rubric. Mean total performance increased by 36.7 points (95% CI: 31.4–42.1; p < 0.001), and the proportion of high-performing students rose from 0% to 70%. Gains were significant across all domains, most notably in opening the encounter, closure, and diabetes specific explanation. Multiple regression showed that lower baseline empathy (β = −0.41, p = 0.005) and higher digital self-efficacy (β = 0.35, p = 0.016) independently predicted greater improvement; gender had only a marginal effect. Cluster analysis revealed three learner profiles, with the highest-gain group characterized by low empathy and high digital self-efficacy. Inter-rater reliability was excellent (ICC ≈ 0.90). These findings provide empirical evidence that GenAI-mediated training can meaningfully enhance diagnostic communication and may serve as a scalable, individualized adjunct to conventional medical education. Full article

Ventilation in subway and railway tunnels is a critical safety component, especially during fire emergencies, where effective smoke and heat management is essential for successful evacuation and firefighting efforts. The Subway Environmental Simulation (SES, Version 4.1) model is widely used for predicting airflow and thermal conditions during fire events, but its accuracy in real-world applications requires validation. This study compares SES predictions with experimental data from the Memorial Tunnel fire ventilation tests to evaluate its performance in simulating the effects of jet fans on longitudinal ventilation. The analysis focuses on SES’s ability to predict flow rate and temperature distributions. Results showed reasonable agreement between SES-predicted airflows and temperatures. However, SES tended to underpredict temperatures upstream and near the fire source, indicating a limitation in simulating thermal behavior close to the fire. These findings suggest that SES can be a reliable tool for tunnel ventilation design if certain safety margins, based on the error values identified in this study, are considered. Nonetheless, further improvements are necessary to enhance its accuracy, particularly in modeling heat transfer dynamics and the impact of fire-induced temperature changes. Future work should focus on conducting additional full-scale test validations and model refinements to improve SES’s predictive capabilities for fire safety planning. Full article

The safe and reliable operation of autonomous vehicles is contingent on comprehensive testing. However, the operational scenarios are inexhaustible. Corner cases, which critically influence autonomous vehicle safety, occur at an extremely low probability and follow a long-tail distribution. Corner cases can be defined as combinations of driving task and scenario elements. These scenarios are characterized by low probability, high risk, and a tendency to reveal functional limitations inherent to autonomous driving systems, triggering anomalous behavior. This study constructs a novel corner case dataset using naturalistic driving data, specifically tailored for autonomous vehicle testing. A scenario marginality quantification method is designed to analyze multi-source naturalistic driving data, enabling efficient extraction of corner cases. Heterogeneous scenarios are systematically transformed, resulting in a dataset characterized by diverse interaction behaviors and standardized formatting. The results indicate that the scenario marginality of the dataset constructed in this study is 2.78 times that of mainstream naturalistic driving datasets, and the scenarios exhibit considerable diversity. The trajectory and velocity fluctuations, quantified at 0.013 m and 0.021 m/s, respectively, are consistent with the kinematic characteristics of real-world driving scenarios. These results collectively demonstrate the dataset’s high marginality, diversity, and applicability. Full article

Objectives: Surface-Guided Radiation Therapy (SGRT) has been widely adopted in breast cancer radiotherapy, particularly for improving setup accuracy and motion management. Recently, its application in lung cancer has attracted growing interest due to similar needs for precision. This study investigates the feasibility and clinical utility of SGRT in lung cancer treatment, focusing on its effectiveness in patient setup and real-time motion monitoring under frameless immobilization conditions. Materials and Methods: A total of 204 treatment records from 17 patients with primary lung cancer who underwent radiotherapy at Korea University Guro Hospital between October 2024 and April 2025 were retrospectively analyzed. Patients were initially positioned using the Identify system (Varian) in the CT suite, with surface data transferred to the treatment room system. Alignment was performed to within ±1 cm and ±2° across six degrees of freedom. Cone-beam CT (CBCT) was acquired prior to treatment for verification, and treatment commenced when the Distance to Correspondence Surface (DCS) was ≤0.90. Setup deviations from the Identify system were recorded and compared with CBCT in three translational axes to evaluate positioning accuracy and PTV displacement. Results and Conclusions: The Identify system was shown to provide high setup accuracy and reliable real-time motion monitoring in lung cancer radiotherapy. Its ability to detect patient movement and automatically interrupt beam delivery contributes to enhanced treatment safety and precision. In addition, even though the maximum longitudinal (Lng) shift reached up to −1.83 cm with surface-guided setup, and up to 1.78 cm (Lat) 5.26 cm (Lng), 9.16 cm (Vrt) with CBCT-based verification, the use of Identify’s auto-interruption mode (±1 cm in translational axes, ±2° in rotational axes) allowed treatment delivery with PTV motion constrained within ±0.02 cm. These results suggest that, due to significant motion in the longitudinal direction, appropriate PTV margins should be considered during treatment planning. The Identify system enhances setup accuracy in lung cancer patients using a surface-guided approach and enables real-time tracking of intra-fractional errors. SGRT, when implemented with systems such as Identify, shows promise as a feasible alternative or complement to conventional IGRT in selected lung cancer cases. Further studies with larger patient cohorts and diverse clinical settings are warranted to validate these findings. Full article

With the large-scale integration of high-penetration distributed photovoltaic (DPV) into distribution networks, its output volatility and reverse power flow characteristics are prone to causing voltage violations, necessitating the accurate identification of weak nodes to enhance operational reliability. This paper investigates the definition, quantification criteria, and multi-indicator comprehensive determination methods for weak nodes in distribution networks. A multi-criteria assessment method integrating voltage deviation rate, sensitivity analysis, and power margin has been proposed. This method quantifies the node disturbance resistance and comprehensively evaluates the vulnerability of voltage stability. Simulation validation based on the IEEE 33-node system demonstrates that the proposed method can effectively identify the distribution patterns of weak nodes under different penetration levels (20~80%) and varying numbers of DPV access points (single-point to multi-point distributed access scenarios). The study reveals the impact of increased penetration and dispersed access locations on the migration characteristics of weak nodes. The research findings provide a theoretical basis for the planning of distribution networks with high-penetration DPV, offering valuable insights for optimizing the siting of volatile loads such as electric vehicle (EV) charging stations while considering both grid safety and the demand for distributed energy accommodation. Full article