Accurate geometry reconstruction of vascular structures using implicit splines

3-D visualization of blood vessel from standard medical datasets (e.g. CT or MRI) play an important role in many clinical situations, including the diagnosis of vessel stenosis, virtual angioscopy, vascular surgery planning and computer aided vascular surgery. However, unlike other human organs, the vasculature system is a very complex network of vessel, which makes it a very challenging task to perform its 3-D visualization. Conventional techniques of medical volume data visualization are in general not well-suited for the above-mentioned tasks. This problem can be solved by reconstructing vascular geometry. Although various methods have been proposed for reconstructing vascular structures, most of these approaches are model-based, and are usually too ideal to correctly represent the actual variation presented by the cross-sections of a vascular structure. In addition, the underlying shape is usually expressed as polygonal meshes or in parametric forms, which is very inconvenient for implementing ramification of branching. As a result, the reconstructed geometries are not suitable for computer aided diagnosis and computer guided minimally invasive vascular surgery. In this research, we develop a set of techniques associated with the geometry reconstruction of vasculatures, including segmentation, modelling, reconstruction, exploration and rendering of vascular structures. The reconstructed geometry can not only help to greatly enhance the visual quality of 3-D vascular structures, but also provide an actual geometric representation of vasculatures, which can provide various benefits. The key findings of this research are as follows: 1. A localized hybrid level-set method of segmentation has been developed to extract the vascular structures from 3-D medical datasets. 2. A skeleton-based implicit modelling technique has been proposed and applied to the reconstruction of vasculatures, which can achieve an accurate geometric reconstruction of the vascular structures as implicit surfaces in an analytical form. 3. An accelerating technique using modern GPU (Graphics Processing Unit) is devised and applied to rendering the implicitly represented vasculatures. 4. The implicitly modelled vasculature is investigated for the application of virtual angioscopy

Which strategy is better for linkage analysis: single-nucleotide polymorphisms or microsatellites? Evaluation by identity-by-state – identity-by-descent transformation affected sib-pair method on GAW14 data

The central issue for Genetic Analysis Workshop 14 (GAW14) is the question, which is the better strategy for linkage analysis, the use of single-nucleotide polymorphisms (SNPs) or microsatellite markers? To answer this question we analyzed the simulated data using Duffy's SIB-PAIR program, which can incorporate parental genotypes, and our identity-by-state – identity-by-descent (IBS-IBD) transformation method of affected sib-pair linkage analysis which uses the matrix transformation between IBS and IBD. The advantages of our method are as follows: the assumption of Hardy-Weinberg equilibrium is not necessary; the parental genotype information maybe all unknown; both IBS and its related IBD transformation can be used in the linkage analysis; the determinant of the IBS-IBD transformation matrix provides a quantitative measure of the quality of the marker in linkage analysis. With the originally distributed simulated data, we found that 1) for microsatellite markers there are virtually no differences in types I and II error rates when parental genotypes were or were not used; 2) on average, a microsatellite marker has more power than a SNP marker does in linkage detection; 3) if parental genotype information is used, SNP markers show lower type I error rates than microsatellite markers; and 4) if parental genotypes are not available, SNP markers show considerable variation in type I error rates for different methods

A Computationally Efficient Hybrid Neural Network Architecture for Porous Media: Integrating CNNs and GNNs for Improved Permeability Prediction

Subsurface fluid flow, essential in various natural and engineered processes, is largely governed by a rock's permeability, which describes its ability to allow fluid passage. While convolutional neural networks (CNNs) have been employed to estimate permeability from high-resolution 3D rock images, our novel visualization technology reveals that they occasionally miss higher-level characteristics, such as nuanced connectivity and flow paths, within porous media. To address this, we propose a novel fusion model to integrate CNN with the graph neural network (GNN), which capitalizes on graph representations derived from pore network model to capture intricate relational data between pores. The permeability prediction accuracy of the fusion model is superior to the standalone CNN, whereas its total parameter number is nearly two orders of magnitude lower than the latter. This innovative approach not only heralds a new frontier in the research of digital rock property predictions, but also demonstrates remarkable improvements in prediction accuracy and efficiency, emphasizing the transformative potential of hybrid neural network architectures in subsurface fluid flow research

The Role of Chinese Financial Industry in Promoting Reform and Opening up and Serving the Real Economy in the New Era

Finance plays an important role in the economic development of the new era. In the historical process of further deepening reform and opening up, the financial industry should upgrade the level of supervision and strengthen the ability of risk prevention so as to realize the strategic goal of serving the real economy. Based on background, it's necessary to analyze the limited openness and protectionist behavior of the financial industry in developed countries, and then review the important achievements of Chinese financial industry in opening up and reform. In the new historical period, China should attach great importance to the work of financial security from the basic national conditions, improve the level of supervision and risk prevention, strengthen abilities of technological innovation, especially gradually expand the two-way reciprocal opening in the financial field, and introduce social capital into the real economy through deepening reform and effective use of financial means, so that finance can return to serving the real economy. The aim is to promote the healthy development of the national economy in the new era, and constantly improve Chinese comprehensive competitiveness in the world economic system. </p

Measuring the UHE cosmic-ray composition with tracking detectors in air shower arrays

Measuring the angles of muons and electrons in air showers is proposed as a method for studying the primary cosmic-ray mass composition near the knee of the cosmic-ray energy spectrum at a few $10^{15}$ eV. Conventional tracking detectors at existing air shower arrays could serve this purpose, like the CRT detectors at the HEGRA array. When the average radial muon angles are examined as a function of shower core distance, the experimental resolution can be very well calibrated from the tangential angle distribution. The method is particularly promising for measuring changes in the average mass number of the primary cosmic rays with energy. The method is described and experimental and theoretical constraints are discussed.Comment: 14 pages, 7 figures included. Accepted by Astroparticle Physics. This paper and descriptions of the CRT detectors are also available via http://eu6.mpi-hd.mpg.de/CRT/CRT-eprints.htm

Jitter decomposition in ring oscillators

Abstract — It is important to separate random jitter from de-terministic jitter to quantify their contributions to the total jit-ter. This paper identifies the limitations of the existing method-ologies for jitter decomposition, and develops a new and efficient approach using time lag correlation functions to decompose dif-ferent jitter components. The theory of the approach is developed and it is applied to a ring oscillator simulated in a 0.6-um AMI CMOS process. Results show good agreement between the theory and hspice simulation. I

Delay-Dependent Fuzzy Hyperbolic Model Based on Data-Driven Guaranteed Cost Control for a Class of Nonlinear Continuous-Time Systems with Uncertainties

This paper develops the fuzzy hyperbolic model with time-varying delays guaranteed cost controller design via state-feedback for a class of nonlinear continuous-time systems with parameter uncertainties. A nonlinear quadratic cost function is developed as a performance measurement of the closed-loop fuzzy system based on fuzzy hyperbolic model with time-varying delays. Some sufficient conditions for the existence of such a fuzzy hyperbolic model based on data-driven guaranteed cost controller via state feedback are presented by a set of linear matrix inequalities (LMIs). A simulation example is provided to illustrate the effectiveness of the proposed approach