Search Results (238)

Submerged floating tunnel (SFT) may be subjected to sudden explosive loads such as internal vehicle explosions, terrorist attacks, and external explosions during operation. Based on the Arbitrary Lagrange–Euler (ALE) method, the locally truncated SFT model and fluid–structure interaction model of internal air and external water are established. Spherical explosives are used to simulate the destructive impact of internal explosions at different positions of the road inside the SFT and key positions at the bottom of the road. The results show that the peak accelerations at the monitoring points caused by the explosions of vehicles on the road rapidly decay within a range of three times the radius of the SFT, and circularly distributed damage appears on the explosion-facing side of the road surface. Longitudinal extensional damage occurs at the junction of the road surface and the SFT wall as well as the bottom supporting wall. Longitudinal cracks appear on the SFT wall. The peak accelerations at the monitoring points of the internal road caused by the concealed bomb at the bottom of the SFT rapidly decay within a range of twice the radius of the SFT, and the damage to the SFT is mainly concentrated on the road surface and the supporting wall. The most dangerous direction of external underwater explosion is determined to be directly below the SFT. When the scaled distance of the explosion is less than 0.543 m/kg^1/3, the accelerations at the monitoring points of the internal road show a single-peak trend with rapid rise and decay, and circumferential through-cracks appear on the SFT wall. The supporting wall connecting the SFT wall and the internal road transmits stress to the road, causing extensive damage. Full article

In the present study, a novel air-floating tripod bucket foundation (AFTBF) with taut mooring is proposed. The mechanical and motion response characteristics of this foundation were investigated through model tests. Furthermore, a parametric study was performed on the factors influencing the RAOs of mooring tension, air cushion pressure, as well as motion in the surge, heave, and pitch directions. The conclusion of this research is as follows: mooring tension, air cushion pressure, and pitch angle exhibit wave-frequency responses in small periods and low-frequency responses in large periods. Surge response is characterized by dual-peak features, while heave response predominantly demonstrates wave-frequency characteristics. As draft increases, the air cushion pressure inside the buckets exhibits a decreasing trend. Changes in water depth have more pronounced impacts on mooring tension and motion responses than on air cushion pressure. The impacts of changes in mooring distance and water depth on mechanical and motion responses are significantly more pronounced than those induced by changes in draft. These findings provide a critical foundation for the optimal design of this foundation in water depths of 30–50 m. Full article

Methane emissions from freshwater reservoirs represent a globally relevant greenhouse gas source, which are estimated to range from 3% to 10% of all global anthropogenic methane emissions. However, there is high uncertainty in estimating reservoir emissions on local to global scales due to a combination of data paucity in key regions, particularly in the Southern Hemisphere, and challenges monitoring emission pathways. The key to improved spatially and temporally representative estimation of emission rates is to better understand the primary drivers of emission pathways, in particular, ebullition. We examine ebullition from 15 freshwater storages located in the Southern Hemisphere subtropical (South East Queensland) and temperate (Tasmania) regions using a combination of optical methane detection to develop the high-resolution mapping of ebullition zones and floating chamber incubation within ebullition zones to quantify areal emission rates. We demonstrate the equivalent water level, through air pressure or physical water level change, as a key driver of ebullition and examine the implications for spatially and temporally representative estimation of reservoir emissions. This study represents the largest broadscale ebullition survey undertaken across Australian temperate and subtropical reservoirs. The study findings are of broad relevance to scientists and corporate and government entities navigating the complexities of estimating greenhouse gas emissions from reservoirs and related policy instruments. Full article

Background/Objectives: Increasing the bioavailability of poorly absorbed drugs is a continuous challenge in modern pharmaceutical technology. This is due to the problematic nature of BCS class IV active pharmaceutical ingredients: these drugs possess poor solubility and membrane permeability. Moreover, many undergo immediate efflux and/or rapid systemic metabolism after absorption. This project aimed to improve the bioavailability of BCS class IV drugs by formulating gastroretentive self-emulsifying systems using curcumin as a model drug. Methods: The base of the systems was created by melting emulsifying agents, dissolution retardants, and PEGs together. Curcumin was added after the mixture was cooled slightly. Aqueous dispersions of several compositions were characterized by dynamic light scattering. After screening these results, the viscosities of the selected formulations were evaluated. Dissolution retardants were selected and added to the most superior samples, and their dissolution profiles were compared. Gastroretention of the final formulation was achieved by dispersing air in the molten system through melt foaming; internal structure was assessed by microCT, and physicochemical properties by PXRD and DSC. Cytotoxicity was measured in Caco-2 cells using MTT and Neutral Red assays, and transcellular transport was also studied. Results: Based on these results, a homogeneous gastric floating system was developed. We observed an advantageous cytotoxic profile and increased bioavailability. Conclusions: Overall, we were able to create a self-emulsifying gastroretentive formulation displaying extended release and gastric retention with a low amount of cost-efficient excipients. Full article

Shaft alignment is adversely affected by the increasingly severe coupled hull–raft deformation in deep-diving, highly integrated submersibles, thereby compromising operational safety and potentially amplifying vibration noise. To address to this issue, this paper investigates an intelligent alignment control method for the floating raft air spring mounting system (ASMS) applied to marine propulsion unit (MPU) under coupled hull–raft deformation conditions. A multi-objective alignment control algorithm was developed based on the NSGA-II optimization method within an N-step receding horizon optimal control framework, enabling simultaneous achievement of shaft alignment attitude adjustment, hull deformation compensation, raft deformation suppression, and pneumatic energy consumption. Experimental validation was conducted on two distinct ASMS prototypes to evaluate the control algorithm. Tests performed on the ASMS for MPU (MPU-ASMS) prototype demonstrated effective compensation of hull-induced deformations, maintaining shaft alignment offsets within ±0.3 mm and angularities within ±0.5 mm/m. Concurrently, experiments on the floating raft ASMS for the stern compartment (SC-FR-ASMS) achieved precise control of axial offsets within ±0.3 mm, angularities within ±0.5 mm/m, and vertical displacements of critical monitoring points within ±1 mm. The adaptive control strategy additionally proved effective in suppressing raft deformation while simultaneously optimizing pneumatic energy consumption. This research provides robust theoretical and technical foundations for intelligent vibration isolation systems in deep-sea equipment to accommodate extreme-depth-induced hull deformation and large-scale raft deformation. Full article

Plastic pollution in river systems is a growing concern, especially in the Mekong Delta, where complex tidal dynamics facilitate downstream transport of plastic waste into the marine environment. This study assessed the density, composition, and temporal variability of floating plastic waste in the Hau River, approximately 30 km upstream of the Tran De River estuary. Floating net traps were deployed during both ebb and flood tides to quantify plastic waste with simultaneous meteorological and hydrological monitoring. The findings highlight that key meteorological factors, such as air temperature, humidity, wind speed, and wind direction, were found to indirectly influence plastic transport by altering surface currents and promoting plastic degradation. Meanwhile, hydrological conditions, especially tidal variability, play a direct and dominant role in determining the spatial and temporal distribution of plastic waste. Plastic debris was diverse in terms of items during both tidal phases. Although the number of plastic pieces was higher at ebb tide (134.33 pieces/h), the volume and concentration of plastic were greater at flood tide (1.22 kg/h and 0.73 kg/m³) than at ebb tide (0.81 kg/h and 0.29 kg/m³). Macroplastic debris was almost dominant during both ebb tide (97.29%) and flood tide (93.96%) compared to megaplastic and mesoplastic size. These findings highlight the importance of integrating tidal and climate factors into plastic waste management and support targeted interventions to reduce plastic discharge into coastal ecosystems. Full article

Partial discharge (PD) is not only a critical indicator but also a major accelerating factor of insulation degradation in power transformers. Accurate identification of PD types is essential for diagnosing insulation defects and locating faults in transformers. Traditional methods based on phase-resolved partial discharge (PRPD) patterns typically rely on expert interpretation and manual feature extraction, which are increasingly being supplanted by Convolutional Neural Networks (CNNs) due to their ability to automatically extract features and deliver high classification accuracy. However, the inherent subtlety and diversity of characteristic differences among PRPD patterns, coupled with substantial noise resulting from complex electromagnetic interference, present significant hurdles to achieving accurate identification. This paper proposes a transformer partial discharge identification method based on Deep Residual Shrinkage Network (DRSN) to address these challenges. The method integrates dual-path feature extraction to capture both local and global features, incorporates a channel-domain adaptive soft-thresholding mechanism to effectively suppress noise interference, and utilizes the Focal Loss function to enhance the model’s attention to hard-to-classify samples. To validate the proposed method, given the scarcity of diverse real-world transformer PD data, an experimental platform was utilized to generate and collect PD data by artificially simulating various discharge defect models, including tip discharge, surface discharge, air-gap discharge and floating discharge. Data diversity was then enhanced through sample augmentation and noise simulation, to minimize the gap between experimental data and real-world on-site data. Experimental results demonstrate that the proposed method achieves superior partial discharge recognition accuracy and strong noise robustness on the experimental dataset. For future work, it is essential to collect more real transformer PD data to further validate and strengthen the model’s generalization capability, thereby ensuring its robust performance and applicability in practical scenarios. Full article

The National Adaptation Plan of Bangladesh identifies eleven climate-stressed zones, placing nearly 100 million people at high risk of climate-related hazards. Vulnerable groups such as the poor, floating populations, daily laborers, and slum dwellers are particularly affected. However, there is a lack of data on climate-sensitive diseases and related hospital visits in these areas. This study explored the prevalence of such diseases using the Delphi method through focus group discussions with 493 healthcare professionals from 153 hospitals in 156 upazilas across 21 districts and ten zones. Participants were selected by district Civil Surgeons. Key climate-sensitive diseases identified included malnutrition, diarrhea, pneumonia, respiratory infections, typhoid, skin diseases, hypertension, cholera, mental health disorders, hepatitis, heat stroke, and dengue. Seasonal surges in hospital visits were noted, influenced by factors like extreme heat, air pollution, floods, water contamination, poor sanitation, salinity, and disease vectors. Some diseases were zone-specific, while others were widespread. Regions with fewer hospital visits often had higher disease burdens, indicating under-reporting or lack of access. The findings highlight the need for area-specific adaptation strategies and updates to the Health National Adaptation Plan. Strengthening resilience through targeted investment and preventive measures is crucial to reducing health risks from climate change. Full article

Air pollution threatens human health and ecosystems, making timely forecasting essential. The spatiotemporal dynamics of pollutants, shaped by various factors, challenge traditional methods. Therefore, spatiotemporal graph-based deep learning has gained attention for its ability to capture spatial and temporal dependencies within monitoring networks. However, many existing models, despite their high predictive accuracy, face computational complexity and scalability challenges. This study introduces clustered and lightweight spatio-temporal graph convolutional network with gated recurrent unit (ClusLite-STGCN-GRU), a hybrid model that integrates spatial clustering based on pollutant time series for graph construction, Copula-based dependency analysis for selecting relevant pollutants to predict PM_2.5, and graph convolution combined with gated recurrent units to extract spatiotemporal features. Unlike conventional approaches that require learning or dynamically updating adjacency matrices, ClusLite-STGCN-GRU employs a fixed, simple cluster-based structure. Experimental results on Tehran air quality data demonstrate that the proposed model not only achieves competitive predictive performance compared to more complex models, but also significantly reduces computational cost—by up to 66% in training time, 83% in memory usage, and 84% in number of floating-point operations—making it suitable for real-time applications and offering a practical balance between accuracy, interpretability, and efficiency. Full article

This study presents a detailed experimental comparison of the rotordynamic and thermal performance of automotive turbochargers supported by two distinct hydrodynamic bearing configurations: fully floating ring bearings (FFRBs) and semi-floating ring bearings (SFRBs). While both designs are widely used in commercial turbochargers, they exhibit significantly different dynamic behaviors due to differences in ring motion and fluid film interaction. A cold air-driven test rig was employed to assess vibration and temperature characteristics across a range of controlled lubricant conditions. The test matrix included oil supply pressures from 2 bar (g) to 4 bar (g) and temperatures between 30 °C and 70 °C. Rotor speeds reached up to 200 krpm (thousands of revolutions per minute), and data were collected using a high-speed data acquisition system, triaxial accelerometers, and infrared (IR) thermal imaging. Rotor vibration was characterized through waterfall and Bode plots, while jump speeds and thermal profiles were analyzed to evaluate the onset and severity of instability. The results demonstrate that the FFRB configuration is highly sensitive to oil supply parameters, exhibiting strong subsynchronous instabilities and hysteresis during acceleration–deceleration cycles. In contrast, the SFRB configuration consistently provided superior vibrational stability and reduced sensitivity to lubricant conditions. Changes in lubricant supply conditions induced a jump speed variation in floating ring bearing (FRB) turbochargers that was approximately 3.47 times larger than that experienced by semi-floating ring bearing (SFRB) turbochargers. Furthermore, IR images and oil outlet temperature data confirm that the FFRB system experiences greater heat generation and thermal gradients, consistent with higher energy dissipation through viscous shear. This study provides a comprehensive assessment of both bearing types under realistic high-speed conditions and highlights the advantages of the SFRB configuration in improving turbocharger reliability, thermal performance, and noise suppression. The findings support the application of SFRBs in high-performance automotive systems where mechanical stability and reduced frictional losses are critical. Full article

Hydrogen is a potential key component of a carbon-neutral energy carrier and an input to marine industrial processes. This study examines the consequences of coupled hydrogen release and marine environmental factors during floating photovoltaic hydrogen production (FPHP) system failures. A validated three-dimensional numerical model of FPHP comprehensively characterizes hydrogen leakage dynamics under varied rupture diameters (25, 50, 100 mm), transient release duration, dispersion patterns, and wind intensity effects (0–20 m/s sea-level velocities) on hydrogen–air vapor clouds. FLACS-generated data establish the concentration–dispersion distance relationship, with numerical validation confirming predictive accuracy for hydrogen storage tank failures. The results indicate that the wind velocity and rupture size significantly influence the explosion risk; 100 mm ruptures elevate the explosion risk, producing vapor clouds that are 40–65% larger than 25 mm and 50 mm cases. Meanwhile, increased wind velocities (>10 m/s) accelerate hydrogen dilution, reducing the high-concentration cloud volume by 70–84%. Hydrogen jet orientation governs the spatial overpressure distribution in unconfined spaces, leading to considerable shockwave consequence variability. Photovoltaic modules and inverters of FPHP demonstrate maximum vulnerability to overpressure effects; these key findings can be used in the design of offshore platform safety. This study reveals fundamental accident characteristics for FPHP reliability assessment and provides critical insights for safety reinforcement strategies in maritime hydrogen applications. Full article

Taking a composite goaf in goaf-side entry retaining as our research focus, a kilogram-level spontaneous combustion experiment was carried out, and limit parameters for coal spontaneous combustion characteristics were assessed. Combined with the key parameters of the site, a numerical model of a multi-area composite goaf was constructed, and the distribution features of the dangerous area for coal spontaneous combustion in the lower layer of in goaf-side entry retaining were determined by means of the upper and lower layer composite superposition division method. The results show that at a floating coal thickness in the goaf of 1.9 m, the lower limit of oxygen concentration C_min, upper limit of air leakage intensity, and corresponding seepage velocity are 6%, 0.282 cm⁻³·s⁻¹·cm⁻², and 11.28 × 10⁻³ m/s respectively. The dangerous area regarding residual coal on the intake side is 23~38 m away from the working face, while that on the return air side is concentrated amid the goaf at 23~75 m, and that on the flexible formwork wall is concentrated at 0~121 m. The research results are of crucial practical importance for the prevention and control of coal spontaneous combustion within a composite goaf. Full article

The force conditions experienced by spacecraft and astronauts in space are vastly different from those in Earth’s gravitational environment, hence it is necessary to conduct adequate micro-low-gravity environment simulation tests on the ground before launch. In this paper, an overview is provided of the current status of micro-low-gravity simulation test technology for spacecraft based on hydrostatic air-bearing. The paper systematically organizes the application of hydrostatic air-bearing technology in micro-low-gravity simulation tests, such as the deployment of space mechanisms, spacecraft GNC (Guidance, Navigation, and Control), on-orbit operations of space manipulators, and astronaut training. It summarizes the principles of air-flotation micro-low-gravity simulation technology in different scenarios and distills suitable solutions for various requirements. Finally, the paper looks forward to the development trends of air-flotation micro-low-gravity simulation test technology and proposes key technical challenges that need to be overcome in aerostatic bearing. Full article

The mine air door is the primary facility for regulating airflow and controlling the passage of personnel and vehicles. Intelligent monitoring of personnel and vehicles within the mine air door system is a crucial measure to ensure the safety of mine operations. To address the issues of slow speed and low efficiency associated with traditional detection methods in mine air door scenarios, this study proposes a CGSW-YOLO man-vehicle monitoring model based on YOLOv8n. Firstly, the Faster Block module, which incorporates partial convolution (PConv), is integrated with the C2f module of the backbone network. This combination aims to minimize redundant calculations during the convolution process and expedite the model’s aggregation of multi-scale information. Secondly, standard convolution is replaced with GhostConv in the backbone network to further reduce the number of model parameters. Additionally, the Slim-neck module is integrated into the neck feature fusion network to enhance the information fusion capability of various feature maps while maintaining detection accuracy. Finally, WIoUv3 is utilized as the loss function, and a dynamic non-monotonic focusing mechanism is implemented to adjust the quality of the anchor frame dynamically. The experimental results indicate that the CGSW-YOLO model exhibits strong performance in monitoring man-vehicle interactions in mine air door scenarios. The Precision (P), Recall (R), and the map@0.5 are recorded at 88.2%, 93.9%, and 98.0%, respectively, representing improvements of 0.2%, 1.5%, and 1.7% over the original model. The Frames Per Second (FPS) has increased to 135.14 f·s⁻¹, reflecting a rise of 35.14%. Additionally, the parameters, the floating point operations per second (FLOPS), and model size are 2.36 M, 6.2 G, and 5.0 MB, respectively. These values indicate reductions of 21.6%, 23.5%, and 20.6% compared to the original model. Through the verification of on-site surveillance video, the CGSW-YOLO model demonstrates its effectiveness in monitoring both individuals and vehicles in scenarios involving mine air doors. Full article

Air-floating supports are the core component of precision gyroscopes. The precision of their assembly directly determines the degree of gyroscope drift and plays a decisive role in the device’s output precision and service life. Existing air-floating support assembly precision analysis techniques have not yet considered interface deformation, resulting in poor theoretical accuracy and large deviations in actual performance. To address the issue of imprecise predictions in assembly precision for air-floating supports under process loads, this study performs a predictive analysis and impact assessment of assembly precision with uncertainties in the connection process. Firstly, based on bolt elastic interactions, a method for calculating interface assembly deformations is proposed and integrated into a predictive model under process loads. Secondly, numerical computations and assembly deformation tests are carried out to confirm the model’s accuracy. Finally, the distribution law of gyroscope drift is analyzed, considering uncertainty in the connection process. The results show that the maximum deviation of the assembly accuracy prediction model for air-floating supports is less than 5%. Drift in air-floating supports under normally distributed loads shows a skewed normal distribution pattern; the mean drift value decreased sequentially from about 0.03023°/h/g to 0.01995°/h/g, which was reduced by 34% with variation in the dispersion of the pretension force (from 15% to 5%). These findings provide novel insights to enhance assembly precision processes and optimize the fabrication of high-end, complex equipment. Full article