NH2+ implantations induced superior hemocompatibility of carbon nanotubes

NH(2)(+) implantation was performed on multiwalled carbon nanotubes (MWCNTs) prepared by chemical vapor deposition. The hemocompatibility of MWCNTs and NH(2)(+)-implanted MWCNTs was evaluated based on in vitro hemolysis, platelet adhesion, and kinetic-clotting tests. Compared with MWCNTs, NH(2)(+)-implanted MWCNTs displayed more perfect platelets and red blood cells in morphology, lower platelet adhesion rate, lower hemolytic rate, and longer kinetic blood-clotting time. NH(2)(+)-implanted MWCNTs with higher fluency of 1 × 10(16) ions/cm(2) led to the best thromboresistance, hence desired hemocompatibility. Fourier transfer infrared and X-ray photoelectron spectroscopy analyses showed that NH(2)(+) implantation caused the cleavage of some pendants and the formation of some new N-containing functional groups. These results were responsible for the enhanced hemocompatibility of NH(2)(+)-implanted MWCNTs

TerrainNet: Visual Modeling of Complex Terrain for High-speed, Off-road Navigation

Effective use of camera-based vision systems is essential for robust performance in autonomous off-road driving, particularly in the high-speed regime. Despite success in structured, on-road settings, current end-to-end approaches for scene prediction have yet to be successfully adapted for complex outdoor terrain. To this end, we present TerrainNet, a vision-based terrain perception system for semantic and geometric terrain prediction for aggressive, off-road navigation. The approach relies on several key insights and practical considerations for achieving reliable terrain modeling. The network includes a multi-headed output representation to capture fine- and coarse-grained terrain features necessary for estimating traversability. Accurate depth estimation is achieved using self-supervised depth completion with multi-view RGB and stereo inputs. Requirements for real-time performance and fast inference speeds are met using efficient, learned image feature projections. Furthermore, the model is trained on a large-scale, real-world off-road dataset collected across a variety of diverse outdoor environments. We show how TerrainNet can also be used for costmap prediction and provide a detailed framework for integration into a planning module. We demonstrate the performance of TerrainNet through extensive comparison to current state-of-the-art baselines for camera-only scene prediction. Finally, we showcase the effectiveness of integrating TerrainNet within a complete autonomous-driving stack by conducting a real-world vehicle test in a challenging off-road scenario

Application of analytic hierarchy process-based model of Ratio of Comprehensive Cost to Comprehensive Profit (RCCCP) in pest management

Ratio of Comprehensive Cost to Comprehensive Profit Analytic hierarchy process Protected horticultural fields Pest management Sustainable development

An assessment method of rail corrugation based on wheel–rail vertical force and its application for rail grinding

In practice, the assessment and treatment of rail corrugation are quantitatively based on the corrugation depth. Wheel–rail vertical forces (WRVF), as a direct reflection of wheel–rail interaction, can give expression to the corrugation depth and thus serve as a key parameter for assessing the corrugation. In this paper, we propose an evaluation method for rail corrugation based on the WRVF. First, a 3D wheel–rail dynamic finite element (FE) model was developed with typical parameters of CRTS II slab track and CRH3 vehicle for high-speed railways in China. The accuracy of the model was then validated with the measured WRVF data in the field. Second, using the validated model, the time–frequency domain distribution of WRVF (vehicle speed: 300 km/h) was obtained with consideration of the corrugation wavelength in the range of 40–180 mm. The non-linear least squares method and rational equation were used to fit the function between the large value of WRVF and the corrugation depth value under the conditions of different corrugation wavelengths. Next, effects of the Pinned–Pinned resonance frequency and vibration mode on the fitted parameters were analysed, by which an indicator for corrugation treatment (grinding) was proposed. Finally, the indicator was applied in the monitoring of rail corrugation for high-speed railway lines in the field. The results show that the misjudgement rate of rail grinding decisions (using the proposed indicator) is low with the accuracy at 92.5%. The proposed method can provide a basis for the rail corrugation evaluation and grinding decisions-making.</p

Kernel Density Derivative Estimation of Euler Solutions

Conventional Euler deconvolution is widely used for interpreting profile, grid, and ungridded potential field data. The Tensor Euler deconvolution applies additional constraints to the Euler solution using all gravity vectors and the full gravity gradient tensor. These algorithms use a series of different-sized moving windows to yield many solutions that can be employed to estimate the source location from the entire survey area. However, traditional discrimination techniques ignore the interrelation among the Euler solutions, so they cannot be employed to separate adjacent targets. To overcome this difficulty, we introduced multivariate Kernel Density Derivative Estimation (KDDE) as an extension of Kernel Density Estimation, which is a mathematical process to estimate the probability density function of a random variable. The multivariate KDDE was tested on a single cube model, a single cylinder model, and three composite models consisting of two cubes with various separations using gridded data. The probability value calculated by the multivariate KDDE was used to discriminate spurious solutions from the Euler solution dataset and isolate adjacent geological sources. The method was then applied to airborne gravity data from British Columbia, Canada. Then, the results of synthetic models and field data show that the proposed method can successfully locate meaningful geological targets