Preferential Growth of Semiconducting Single-Walled Carbon Nanotubes on Substrate by Europium Oxide

In this paper, we have demonstrated that europium oxide (Eu2O3) is a new type of active catalyst for single-walled carbon nanotubes (SWNTs) growth under suitable conditions. Both random SWNT networks and horizontally aligned SWNT arrays are efficiently grown on silicon wafers. The density of the SWNT arrays can be altered by the CVD conditions. This result further provides the experimental evidence that the efficient catalyst for SWNT growth is more size dependent than the catalysts themselves. Furthermore, the SWNTs from europium sesquioxides have compatibly higher quality than that from Fe/Mo catalyst. More importantly, over 80% of the nanotubes from Eu2O3 are semiconducting SWNTs (s-SWNTs), indicating the preferential growth of s-SWNTs from Eu2O3. This new finding could open a way for selective growth of s-SWNTs, which can be used as high-current nanoFETs and sensors. Moreover, the successful growth of SWNTs by Eu2O3 catalyst provides new experimental information for understanding the preferential growth of s-SWNTs from Eu2O3, which may be helpful for their controllable synthesis

LNG Tank Sloshing Simulation of Multidegree Motions Based on Modified 3D MPS Method

Modified 3D Moving Particle Semi-Implicit (MPS) method is used to complete the numerical simulation of the fluid sloshing in LNG tank under multidegree excitation motion, which is compared with the results of experiments and 2D calculations obtained by other scholars to verify the reliability. The cubic spline kernel functions used in Smoothed Particle Hydrodynamics (SPH) method are adopted to reduce the deviation caused by consecutive two times weighted average calculations; the boundary conditions and the determination of free surface particles are modified to improve the computational stability and accuracy of 3D calculation. The tank is under forced multidegree excitation motion to simulate the real conditions of LNG ships, the pressures and the free surfaces at different times are given to verify the accuracy of 3D simulation, and the free surface and the splashed particles can be simulated more exactly

Experimental Investigation on Heat Transfer and Pressure Drop of Supercritical Carbon Dioxide in a Mini Vertical Upward Flow

Experiments on the convection heat transfer and pressure drop of supercritical carbon dioxide in a mini vertical upward flow were investigated in a smooth tube with an inner diameter of 2 mm. The experiments were conducted with pressures ranging from 7.62 to 8.44 MPa, mass fluxes ranging from 600 to 1600 kg·m−2·s−1, and heat flux ranging from 49.3 to 152.3 kW·m−2. Results show that the peak of heat transfer occurs when the bulk fluid temperature is below the proposed critical temperature and the wall temperature is above the proposed critical temperature. For the 2 mm vertical upward flow, the radial buoyancy effects are relatively weak, and the axial thermal acceleration effect cannot be negligible. In this study, a new modified Jackson correlation for the supercritical carbon dioxide is proposed for convective heat transfer. To reflect the effect of flow acceleration on heat transfer, a dimensionless heat flux was introduced to construct a new semi-correlation of heat transfer. The new correlation of friction factor taking into account the variation of density and dynamic viscosity was proposed with 146 experimental data within a ±20% error band

Marine Organism Detection and Classification from Underwater Vision Based on the Deep CNN Method

Seabed fishing depends on humans in common, for instance, the sea cucumber, sea urchin, and scallop fishing, which is always a very dangerous task. Considering the underwater complex environment conditions such as low temperature, dim vision, and high pressure, collecting the marine products using underwater robots is commonly regarded as a feasible solution. The key technique of the underwater robot development is to detect and locate the main target from underwater vision. This research is based on the deep convolutional neural network (CNN) to realize the target recognition from underwater vision. The RPN (Region Proposal Network) is used to optimize the feature extraction capability. Deep learning dataset is prepared using an underwater video obtained from a sea cucumber fishing ROV (Remote Operated Vehicle). The inspiration of the network structure and the improvements come from the Faster RCNN and Hypernet method, and for the underwater dataset, the method proposed in this paper shows a good performance of recall and object detection accuracy. The detection runs with a speed of 17 fps on a GPU, which is applicable to be used for real-time processing

Experimental Investigation on Heat Transfer and Pressure Drop of Supercritical Carbon Dioxide in a Mini Vertical Upward Flow

Experiments on the convection heat transfer and pressure drop of supercritical carbon dioxide in a mini vertical upward flow were investigated in a smooth tube with an inner diameter of 2 mm. The experiments were conducted with pressures ranging from 7.62 to 8.44 MPa, mass fluxes ranging from 600 to 1600 kg·m−2·s−1, and heat flux ranging from 49.3 to 152.3 kW·m−2. Results show that the peak of heat transfer occurs when the bulk fluid temperature is below the proposed critical temperature and the wall temperature is above the proposed critical temperature. For the 2 mm vertical upward flow, the radial buoyancy effects are relatively weak, and the axial thermal acceleration effect cannot be negligible. In this study, a new modified Jackson correlation for the supercritical carbon dioxide is proposed for convective heat transfer. To reflect the effect of flow acceleration on heat transfer, a dimensionless heat flux was introduced to construct a new semi-correlation of heat transfer. The new correlation of friction factor taking into account the variation of density and dynamic viscosity was proposed with 146 experimental data within a ±20% error band

Bow Flare Water Entry Impact Prediction and Simulation Based on Moving Particle Semi-Implicit Turbulence Method

Prandtl’s mixing length method and the k-epsilon method are introduced into the Moving Particle Semi-Implicit (MPS) method for the purpose of modeling turbulence effects associated with water entries of two-dimensional (2D) bow flare section. The presented numerical method is validated by comparing its numerical prediction with experimental data and other numerical results obtained from the Boundary Element Method (BEM). The time histories of the pressure and the vertical slamming force acting on the dropping ship section subjected to various conditions with different dropping velocity and inclined angles are analyzed. The results show that both the pressure and the vertical slamming force are in good agreement with the experimental data

Underwater Image Processing and Object Detection Based on Deep CNN Method

Due to the importance of underwater exploration in the development and utilization of deep-sea resources, underwater autonomous operation is more and more important to avoid the dangerous high-pressure deep-sea environment. For underwater autonomous operation, the intelligent computer vision is the most important technology. In an underwater environment, weak illumination and low-quality image enhancement, as a preprocessing procedure, is necessary for underwater vision. In this paper, a combination of max-RGB method and shades of gray method is applied to achieve the enhancement of underwater vision, and then a CNN (Convolutional Neutral Network) method for solving the weakly illuminated problem for underwater images is proposed to train the mapping relationship to obtain the illumination map. After the image processing, a deep CNN method is proposed to perform the underwater detection and classification, according to the characteristics of underwater vision, two improved schemes are applied to modify the deep CNN structure. In the first scheme, a 1∗1 convolution kernel is used on the 26∗26 feature map, and then a downsampling layer is added to resize the output to equal 13∗13. In the second scheme, a downsampling layer is added firstly, and then the convolution layer is inserted in the network, the result is combined with the last output to achieve the detection. Through comparison with the Fast RCNN, Faster RCNN, and the original YOLO V3, scheme 2 is verified to be better in detecting underwater objects. The detection speed is about 50 FPS (Frames per Second), and mAP (mean Average Precision) is about 90%. The program is applied in an underwater robot; the real-time detection results show that the detection and classification are accurate and fast enough to assist the robot to achieve underwater working operation

Advanced Underwater Measurement System for ROVs: Integrating Sonar and Stereo Vision for Enhanced Subsea Infrastructure Maintenance

In the realm of ocean engineering and maintenance of subsea structures, accurate underwater distance quantification plays a crucial role. However, the precision of such measurements is often compromised in underwater environments due to backward scattering and feature degradation, adversely affecting the accuracy of visual techniques. Addressing this challenge, our study introduces a groundbreaking method for underwater object measurement, innovatively combining image sonar with stereo vision. This approach aims to supplement the gaps in underwater visual feature detection with sonar data while leveraging the distance information from sonar for enhanced visual matching. Our methodology seamlessly integrates sonar data into the Semi-Global Block Matching (SGBM) algorithm used in stereo vision. This integration involves introducing a novel sonar-based cost term and refining the cost aggregation process, thereby both elevating the precision in depth estimations and enriching the texture details within the depth maps. This represents a substantial enhancement over existing methodologies, particularly in the texture augmentation of depth maps tailored for subaquatic environments. Through extensive comparative analyses, our approach demonstrates a substantial reduction in measurement errors by 1.6%, showing significant promise in challenging underwater scenarios. The adaptability and accuracy of our algorithm in generating detailed depth maps make it particularly relevant for underwater infrastructure maintenance, exploration, and inspection

Negative causal exploration of systemic sclerosis: a Mendelian randomization analysis

Abstract Systemic sclerosis (SSc), also known as scleroderma, is an autoimmune-related connective tissue disease with a complex and unknown pathophysiological mechanism with genes association. Several articles have reported a high prevalence of thyroid disease in SSc patients, while one study suggested a potential contribution of appendicitis to the development of SSc. To investigate this causal association, we conducted Mendelian randomization (MR) analysis using instrumental variables (IVs) to assess exposure and outcome. In the MR study involving two cohorts, all analyses were conducted using the TwoSampleMR package in R (version 4.3.0). Single nucleotide polymorphisms (SNPs) meeting a statistically significant threshold of 5E−08 were included in the analysis. Multiple complementary approaches including MR-IVW, MR-Egger, weighted median, simple mode, and weighted mode were employed to estimated the relationship between the exposure and outcome. Leave-one-out analysis and scatter plots were utilized for further investigation. Based on the locus-wide significance level, all of the MR analysis consequences manifested no causal association between the risk of appendicitis with SSc (IVW OR 0.319, 95% CI 0.063–14.055, P = 0.966). Negative causal effects of autoimmune thyroiditis (AT) on SSc (IVW OR 0.131, 95% CI 0.816–1.362, P = 0.686), Graves’ disease (GD) on SSc (IVW OR 0.097, 95% CI 0.837–1.222, P = 0.908), and hypothyroidism on SSc (IVW OR 1.136, 95% CI 0.977–1.321, P = 0.096) were derived. The reverse MR revealed no significant causal effect of SSc on thyroid disease. According to the sensitivity analysis, horizontal pleiotropy was unlikely to distort the causal estimates. The consequences indicated no significant association between AT, GD, and hypothyroidism with SSc. Similarly, there was no observed relationship with appendicitis