Hierarchical CunO n O nanoflowers were synthesized through the laser ablation of a CuO target in NaOH solutions for room-temperature (27 degree celsius) H2S 2 S detection. Notably, the pH value of NaOH solutions influenced both the micro- morphologies and compositions of the CunO n O products, as evidenced by XRD, XPS, SEM and TEM. In high pH solutions, the specific surface area of the CunO n O products increased, their thickness decreased, and the Cu2O 2 O content diminished, resulting in enhanced sensitivity, selectivity and stability of the CunO n O products' response to H2S. 2 S. Notably, the pH14# # sample synthesized using an NaOH solution with a pH value of 14 featured pure CuO nanoflowers comprising slightly curled nanosheets with a thickness of approximately 10 nm. This sensor demonstrated excellent H2S 2 S sensing performance at room temperature, exhibiting a response value of 1.17 for 10 ppb H2S, 2 S, along with high selectivity and good long-term stability. However, after exposure to H2S, 2 S, the resistance of the sensor did not recover to its baseline in air at room temperature. Thermogravimetry results revealed that a temperature of 300 degrees C was effective for recovery of the sensor. Consequently, the operation temperature of the pH14# # sensor was controlled using a micro-hotplate. In the pulse heating mode, the sensor's response to 100 ppb H2S 2 S was 1.5, with a response time of 135 s and a recovery time of 137 s.

Texture mapping is a common technology in the area of computer graphics, it maps the 3D surface space onto the 2D texture space. However, the loose texture space will reduce the efficiency of data storage and GPU memory addressing in the rendering process. Many of the existing methods focus on repacking given textures, but they still suffer from high computational cost and hardly produce a wholly tight texture space. In this paper, we propose a method to optimize the texture space and produce a new texture mapping which is compact based on global parameterization. The proposed method is computationally robust and efficient. Experiments show the effectiveness of the proposed method and the potency in improving the storage and rendering efficiency.

Supine and prone colon registration is challenging due to the change in the patient's position and the consequent severe distortion of the colon shape. We propose a novel method for colon registration in the cylinder domain. Our method uses Teichmuller Map (T-Map) to match the prescribed feature points in supine and prone positions. In addition, we propose a scheme of virtual boundaries for categorizing feature points as constraints for T-Map. We validate our proposed method on a dataset of 40 colon data and demonstrate the superiority of our method over the planar domain-based method.

Ethanol and acetone sensors have a wide variety of applications across different industries. However, it is necessary to improve the performance of such sensors and to understand the underlying mechanisms. In this study, Pd/PdO-WO3 nanoblocks are synthesized via hydrothermal growth and calcination. The Pd and PdO contents of the materials are tuned by varying the Pd doping concentration and annealing temperature. The gas sensing performance of the nanoparticles is investigated, which shows that Pd doping increases the sensitivity and reduces the optimum operating temperature. At 200 degrees C, Pd/PdO-WO3 nanoblocks with different PdO ratios exhibit good sensitivity to acetone and ethanol. However, because PdO is an active catalyst for ethanol oxidation, the oxygen sensitivity increases as the PdO ratio increases. The prepared sensors exhibit good stability and excellent selectivity against a variety of interferents, and the detection limit of the target gas is 100 ppb. The chemical and electronic sensitization of Pd/PdO lowers the activation barrier and improves the gas sensing response. This study demonstrates that the selectivity of a gas sensor can be regulated by controlling the electronic states of the active species.

Virtual colonoscopy plays an important role in polyp detection of colorectal cancer. Noise in the colon data acquisition process can result in topological errors during surface reconstruction. Topological denoising can be employed to remove these errors on surfaces for subsequent geometry processing, such as surface simplification and parameterization. Many methods have been proposed for this task. However, many existing methods suffer from failure in computation of all the non-trivial loops, due to high genus or complex topological structures. In this paper, we propose a novel robust topological denoising method for surfaces based on homotopy theory. The proposed method was evaluated on two datasets of colon meshes. We compared our method with the State-of-the-Art persistent-homology-based method. Our method can successfully compute the loops on all colon data for topological denoising, whereas the persistent homology method fails on some colon data. Moreover, our method detects all loops with shorter lengths than those detected by the persistent homology method. Our experimental results show that the proposed method is effective and robust in topological denoising, and that it has the potential for practical application to virtual colonoscopy.

The inclinometer is a vital tool for monitoring internal displacement in slopes. However, existing fiber-optic inclinometers lack the ability to monitor three-dimensional deformation. To overcome this limitation, this study introduces a novel distributed inclinometer based on Optical Frequency Domain Reflectometer (OFDR) technology. The inclinometer uses Frenet-Serret equations to achieve three-dimensional displacement calculation. To validate the proposed method and the feasibility of the OFDR inclinometer, finite element analysis and experimental testing are conducted. A comparison is made between the proposed method and the conjugate beam and finite difference algorithm. The results demonstrate the reliability of the displacement calculation. The distributed inclinometer developed based on the Frenet-Serret equations and OFDR technology can achieve three-dimensional deformation measurement in space. The measurement results are satisfactory, with the maximum errors in the OFDR inclinometer's measurements per unit length being within 0.45%. This capability enables effective measurement of internal displacement within slopes.

We present a novel, effective method for global indoor scene reconstruction problems by geometric topology. Based on point cloud pairwise registration methods (e.g ICP) or IMU, we focus on the problem of accumulated error for the composition of transformations along any loops. The major technical contribution of this paper is a linear method for the graph optimation, using only solving a Poisson equation. We demonstrate the consistency of our method from Hodge-Helmhotz decomposition theorem and experiments on multiple RGBD datasets of indoor scene. The experimental results also demonstrate that our global registration method runs quickly and provides accurate reconstructions.

This work proposes a rigorous and practical algorithm for quad-mesh generation based the Abel–Jacobi theory of algebraic curves. We prove sufficient and necessary conditions for a flat metric with cone singularities to be compatible with a quad-mesh, in terms of the deck-transformation, then develop an algorithm based on the theorem. The algorithm has two stages: first, a meromorphic quartic differential is generated to induce a T-mesh; second, the edge lengths of the T-mesh are adjusted by solving a linear system to satisfy the deck transformation condition, which produces a quad-mesh.In the first stage, the algorithm pipeline can be summarized as follows: calculate the homology group; compute the holomorphic differential group; construct the period matrix of the surface and Jacobi variety; calculate the Abel–Jacobi map for a given divisor; optimize the divisor to satisfy the Abel–Jacobi condition by integer programming; compute a flat Riemannian metric with cone singularities at the divisor by Ricci flow; isometrically immerse the surface punctured at the divisor onto the complex plane and pull back the canonical holomorphic differential to the surface to obtain the meromorphic quartic differential; construct a motorcycle graph to generate a T-Mesh.In the second stage, the deck transformation constraints are formulated as a linear equation system of the edge lengths of the T-mesh. The solution provides a flat metric with integral deck transformations, which leads to the final quad-mesh.The proposed method is rigorous and practical. The T-mesh and quad-mesh results can be applied for constructing Splines directly. The efficiency and efficacy of the proposed algorithm are demonstrated by experimental results on surfaces with complicated topologies and geometries.

Surface meshing plays a fundamental important role in Visualization and Computer Graphics, which produces discrete meshes to approximate a smooth surface. Many geometric processing tasks heavily depend on the qualities of the meshes, especially the convergence in terms of topology, position, Riemannian metric, differential operators and curvature measures.Normal cycle theory points out that in order to guarantee the convergence of curvature measures, the discrete meshes are required to approximate not only the smooth surface itself, but also the normal cycle of the surface. This theory inspires the development of the remeshing method based on conformal parameterization and planar Delaunay refinement, which uniformly samples the smooth surface, and produces Delaunay triangulations with bounded minimal corner angles. This method ensures the Hausdorff distances between the normal cycles of the resulting meshes and the smooth normal cycle converges to 0, the discrete Gaussian curvature and mean curvature measures of the resulting meshes converge to their counter parts on the smooth surface.In the current work, the conformal parameterization based remeshing algorithm is further improved to speed up the curvature convergence. Instead of uniformly sampling the surface itself, the novel algorithm samples the normal cycle of the surface. The algorithm pipeline is as follows: first, two parameterizations are constructed, one is the surface conformal parameterization based on dynamic Ricci flow, the other is the normal cycle area-preserving parameterization based on optimal mass transportation; second, the normal cycle parameterization is uniformly sampled; third, the Delaunay refinement mesh generation is carried out on the surface conformal parameterization. The produced meshes can be proven to converge to the smooth surface in terms of curvature measures.Experimental results demonstrate the efficiency and efficacy of proposed algorithm, the convergence speeds of the curvatures are prominently faster than those of conventional methods.