The Development of Log Aesthetic Patch and Its Projection onto the Plane

In this work, we introduce a new type of surface called the Log Aesthetic Patch (LAP). This surface is an extension of the Coons surface patch, in which the four boundary curves are either planar or spatial Log Aesthetic Curves (LACs). To identify its versatility, we approximated the hyperbolic paraboloid to LAP using the information of lines of curvature (LoC). The outer part of the LoCs, which play a role as the boundary of the hyperbolic paraboloid, is replaced with LACs before constructing the LAP. Since LoCs are essential in shipbuilding for hot and cold bending processes, we investigated the LAP in terms of the LoC’s curvature, derivative of curvature, torsion, and Logarithmic Curvature Graph (LCG). The numerical results indicate that the LoCs for both surfaces possess monotonic curvatures. An advantage of LAP approximation over its original hyperbolic paraboloid is that the LoCs of LAP can be approximated to LACs, and hence the first derivative of curvatures for LoCs are monotonic, whereas they are non-monotonic for the hyperbolic paraboloid. This confirms that the LAP produced is indeed of high quality. Lastly, we project the LAP onto a plane using geodesic curvature to create strips that can be pasted together, mimicking hot and cold bending processes in the shipbuilding industry.

Modeling analysis and optimization of temperature curve of welding furnace

Reflux welding is widely used in SMT (surface patch technology) During this production process, the quality of the product is essential to maintain the temperature and the furnace speed required by the process. The furnace temperature curve in the furnace is an important form of reaction welding process. In order to improve the process efficiency of the return furnace, the heating welding process model is established based on the Fourier heat conduction law,1 D heat conduction model and Newton cooling law and draws the furnace temperature curve model. Then, the upper boundary of the conveyor speed using the boundary analysis and multiple target planning, and further explore the research and optimization direction of subsequent process flow. At the same time, this paper examines and analyzes the modeling process and results, and effectively demonstrates the scientific nature and accuracy of the model. Finally, the paper analyzes the significance of the above model and research in chip processing.

Hybrid Reconstruction and Evaluation of 3D Model of Equipment Parts for Virtual Maintenance System

The research and application of reverse engineering technology are more and more extensive, but its process is still not fully mature. Aiming at the complexity of equipment parts and the diversity of accuracy requirements, the method of hybrid reconstruction and evaluation of 3D model of equipment parts for virtual maintenance system is illustrated. The general process is given. Taking the two-stage bevel gear worm reducer as the research object, the research on three-dimensional scanning, data processing and model reconstruction is carried out. This paper focuses on the reconstruction process and methods such as regional division, solid model construction based on maximum outer contour, creation of basic surface, patch fitting and so on by taking the bevel gear in the reducer as an example. Finally, 3D comparison, 2D comparison, cross-section accuracy and size of the reconstructed solid model are evaluated. The results show that the hybrid reconstruction method has strong adaptability and high efficiency. Reverse modeling and forward design are organically combined to give full play to their advantages, and the hybrid reconstruction method can effectively reverse the original design intention of the product and improve the parameterization ability of reverse modeling. It can meet the requirements of different precision models developed by virtual maintenance system, structural wall chart and atlas, multimedia and so on.

Structural Comparisons of Cefotaximase (CTX-M-ase) Sub Family 1

The cefotaximase or CTX-M, family of serine-β-lactamases represents a significant clinical concern due to the ability for these enzymes to confer resistance to a broad array of β-lactam antibiotics an inhibitors. This behavior lends CTX-M-ases to be classified as extended spectrum β-lactamases (ESBL). Across the family of CTX-M-ases most closely related to CTX-M-1, the structures of CTX-M-15 with a library of different ligands have been solved and serve as the basis of comparison within this review. Herein we focus on the structural changes apparent in structures of CTX-M-15 in complex with diazabicyclooctane (DABCO) and boronic acid transition state analog inhibitors. Interactions between a positive surface patch near the active site and complementary functional groups of the bound inhibitor play key roles in the dictating the conformations of active site residues. The insights provided by analyzing structures of CTX-M-15 in complex with DABCO and boronic acid transition state analog inhibitors and analyzing existing structures of CTX-M-64 offer opportunities to move closer to making predictions as to how CTX-M-ases may interact with potential drug candidates, setting the stage for the further development of new antibiotics and β-lactamase inhibitors.

Fast Quadratic Shape From Shading With Unknown Light Direction

An algorithm is proposed that extracts 3D shape from shading information in a digital image. The algorithm assumes that there is only a single source of light producing the image, that the surface of the shape giving rise to the image is Lambertian (matte) and that its shape can be locally approximated by a quadratic function. Previous work shows that under these assumptions, robust shape from shading is possible, though slow for large images because a non-linear optimization method is applied in order to estimate local quadratic surface patches from image intensities. The work presented here shows that local quadratic surface patch estimates can be computed, without prior knowledge of the light source direction, via a linear least squares optimization, thus greatly improving the algebraic complexity and run-time of the existing algorithms.

Fast Quadratic Shape From Shading With Unknown Light Direction

An algorithm is proposed that extracts 3D shape from shading information in a digital image. The algorithm assumes that there is only a single source of light producing the image, that the surface of the shape giving rise to the image is Lambertian (matte) and that its shape can be locally approximated by a quadratic function. Previous work shows that under these assumptions, robust shape from shading is possible, though slow for large images because a non-linear optimization method is applied in order to estimate local quadratic surface patches from image intensities. The work presented here shows that local quadratic surface patch estimates can be computed, without prior knowledge of the light source direction, via a linear least squares optimization, thus greatly improving the algebraic complexity and run-time of this existing algorithms.

Fast Quadratic Shape From Shading With Unknown Light Direction

An algorithm is proposed that extracts 3D shape from shading information in a digital image. The algorithm assumes that there is only a single source of light producing the image, that the surface of the shape giving rise to the image is Lambertian (matte) and that its shape can be locally approximated by a quadratic function. Previous work shows that under these assumptions, robust shape from shading is possible, though slow for large images because a non-linear optimization method is applied in order to estimate local quadratic surface patches from image intensities. The work presented here shows that local quadratic surface patch estimates can be computed, without prior knowledge of the light source direction, via a linear least squares optimization, thus greatly improving the algebraic complexity and run-time of this existing algorithms.

Vision-Based Finger Tapping Detection Without Fingertip Observation

A machine learning approach is investigated in this study to detect a finger tapping on a handheld surface, where the movement of the surface is observed visually; however, the tapping finger is not directly visible. A feature vector extracted from consecutive frames captured by a high-speed camera that observes a surface patch is input to a convolutional neural network to provide a prediction label indicating whether the surface is tapped within the sequence of consecutive frames (“tap”), the surface is still (“still”), or the surface is moved by hand (“move”). Receiver operating characteristics analysis on a binary discrimination of “tap” from the other two labels shows that true positive rates exceeding 97% are achieved when the false positive rate is fixed at 3%, although the generalization performance against different tapped objects or different ways of tapping is not satisfactory. An informal test where a heuristic post-processing filter is introduced suggests that the use of temporal history information should be considered for further improvements.

Conformational epitope matching and prediction based on protein surface spiral features

Background A conformational epitope (CE) is composed of neighboring amino acid residues located on an antigenic protein surface structure. CEs bind their complementary paratopes in B-cell receptors and/or antibodies. An effective and efficient prediction tool for CE analysis is critical for the development of immunology-related applications, such as vaccine design and disease diagnosis. Results We propose a novel method consisting of two sequential modules: matching and prediction. The matching module includes two main approaches. The first approach is a complete sequence search (CSS) that applies BLAST to align the sequence with all known antigen sequences. Fragments with high epitope sequence identities are identified and the predicted residues are annotated on the query structure. The second approach is a spiral vector search (SVS) that adopts a novel surface spiral feature vector for large-scale surface patch detection when queried against a comprehensive epitope database. The prediction module also contains two proposed subsystems. The first system is based on knowledge-based energy and geometrical neighboring residue contents, and the second system adopts combinatorial features, including amino acid contents and physicochemical characteristics, to formulate corresponding geometric spiral vectors and compare them with all spiral vectors from known CEs. An integrated testing dataset was generated for method evaluation, and our two searching methods effectively identified all epitope regions. The prediction results show that our proposed method outperforms previously published systems in terms of sensitivity, specificity, positive predictive value, and accuracy. Conclusions The proposed method significantly improves the performance of traditional epitope prediction. Matching followed by prediction is an efficient and effective approach compared to predicting directly on specific surfaces containing antigenic characteristics.