Development of SERS-based aptasensors for respiratory virus diagnosis

Chen, Hao 중앙대학교 대학원 2022 국내박사

The discovery of surfaces enhanced Raman scattering (SERS) is a landmark moment in the history of spectroscopic and analytical techniques. Significant experimental and theoretical research has been dedicated to establishing its applicability in a wide variety of ultrasensitive sensing applications. SERS has developed into a vibrant field of research and technology in the 45 years after its discovery, including biochemistry and biosensing, materials science, polymer science, catalysis, and electrochemistry. SERS in combination with nanotechnology has been recognized as a promising tool in the area of biosensing. The SERS-based assay has great potential as a multiplex detection approach due to the Raman fingerprints and signal enhancement by electromagnetic enhancement and chemical enhancement. In this work, SERS was used as a highly sensitive assay method based on specific aptamers. Aptamers are single-stranded nucleic acids, which functional roles are similar to those of antibodies as bioreceptors. Aptamers have been shown to interact with a wide variety of target molecules, including small organic molecules, proteins, viral particles, whole cells, inorganic compounds and bacteria. Much of the success of aptamers can be attributed to SELEX (systematic evolution of ligands by exponential enrichment). Aptamers and monoclonal antibodies have comparable levels of affinity when it comes to detecting targets. Meanwhile, aptamers could overcome the disadvantages of antibodies, such as, highly immunogenic, time-consuming and expensive to manufacture, and significant batch to batch variation. This work mainly focused on the development of SERS-based aptasensors for respiratory virus diagnosis. Respiratory viruses are the primary cause of illness in humans. Various virus families are capable of infecting and causing disease via the respiratory tract. Due to their capacity to spread via the respiratory route, newly emerging diseases like these have the potential to cause a pandemic and pose significant challenges to global health, with significant morbidity and mortality. Influenza viruses and coronaviruses, including Middle East respiratory syndrome coronavirus (MERS-CoV), severe acute respiratory syndrome coronavirus (SARS-CoV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), have been related to a number of viral pneumonia epidemics worldwide. By developing a better diagnostics method of these infections, we can create strategies to defend against the more severe, potentially pandemic diseases. By taking the advantages of SERS and aptamers, several SERS-based aptasensors were specifically developed and applied for the diagnosis of respiratory viruses, such as influenza A/H1N1 and SARS-CoV-2. In chapter 2, we investigated the application of the SERS-based imaging aptasensor platform for detecting A/H1N1 virus. SERS-based aptasensors display high sensitivity for influenza A/H1N1 virus detection but improved signal reproducibility is required. Therefore, we fabricated a three-dimensional (3D) nano-popcorn plasmonic substrate with multiple hotspots which dramatically enhanced the incident field. Quantitative evaluation of A/H1N1 virus was achieved using the decrease of Raman peak intensity resulting from the release of Cy3-labeled aptamers from nano-popcorn substrate surfaces via the interaction between the aptamer deoxyribonucleic acid (DNA) and A/H1N1 virus. The SERS-based assay for detecting A/H1N1 virus had an estimated limit of detection of 97 PFU/mL and the assay time was estimated to be 20 min. In chapter 3, We developed a new SERS-based aptasensor platform capable of quantifying SARS-CoV-2 lysates with a high sensitivity. In this study, a spike protein DNA aptamer was used as a receptor for the sensible detection of SARS-CoV-2. This technique enables detecting SARS-CoV-2 with a limit of detection (LOD) of less than 10 PFU/mL within 15 min. The results of this study demonstrate the possibility of a clinical application that can dramatically improve the detection limit and accuracy. In chapter 4, in order to quickly and accurately diagnose and distinguish SARS-CoV-2 and influenza A/H1N1 concurrently, we developed a dual-mode SERS-based aptasensor. In that context, DNA aptamers that selectively bind to SARS-CoV-2 and influenza A/H1N1 were jointly immobilized on an Au nanopopcorn substrate. Raman reporters (Cy3 and RRX). Additionally, the internal standard Raman reporter (4-MBA) was introduced to reduce errors caused by variations in the measurement environment. When SARS-CoV-2 or influenza A virus approaches, the corresponding DNA aptamer selectively detaches. Using this SERS-based aptasensor, it is possible to determine whether a patient is infected with SARS-CoV-2 or influenza A. Therefore, these SERS-based aptasensors can be considered conceptually new diagnostic platforms for detecting viral pathogens, that rapidly identifies respiratory diseases to prevent their spread. 표면 증강 라만 산란(SERS)의 발견은 분광 및 분석 기술의 역사에서의 획기적인 순간이다. 다양한 초고감도 감지 응용 분야에서 적용 가능성을 확립하기 위해 상당한 실험적 및 이론적 연구를 수행했다. SERS는 발견 후 45년 동안 생화학 및 바이오 센싱, 재료 과학, 고분자 과학, 촉매 및 전기화학을 포함하는 활발한 연구 및 기술 분야로 발전했다. 나노기술과 결합된 SERS는 바이오 센싱 분야에서 유망한 도구로 인식되어 왔다. SERS 기반 분석은 라만 지문 스펙트럼, 전자기 증강 및 화학적 신호 증강 기능을 가지고 있기 때문에 다중 검사 방법으로서 큰 잠재력을 가지고 있다. 본 연구에서 SERS는 특정한 압타머 기반의 매우 민감한 측정 방법으로 사용된다. 압타머는 단일 사슬 핵산으로, 기능적 역할은 생체 수용체로서의 항체의 역할과 유사하다. 압타머는 작은 유기 분자, 단백질, 바이러스 입자, 전체 세포, 무기 화합물 및 박테리아를 비롯한 다양한 표적 분자와 상호 작용하는 것으로 나타났다. 압타머의 성공은 대부분 SELEX (systematic evolution of ligands by exponential enrichment)에 기인한다. 압타머와 단일 클론 항체는 표적 탐지와 관련하여 비슷한 수준의 친화력을 가지고 있다. 한편, 압타머는 높은 면역원성, 시간 소모적, 제조 비용, 상당한 배치 간 편차와 같은 항체의 단점을 극복할 수 있다. 이 연구는 주로 호흡기 바이러스 진단을 위한 SERS 기반 aptasensor 개발에 중점을 두었다. 호흡기 바이러스는 인류 질병의 주요 원인이다. 여러가지 바이러스는 호흡기를 통해 감염될 수 있고 질병을 일으킬 수 있다. 호흡기를 통한 전파능력으로 말미암아, 이와 같이 새롭게 출현하는 질병들은 대유행을 초래하여 전 세계 건강에 중대한 도전과제를 제기하며, 현저한 발병률과 사망률을 갖고 있다. 중동호흡기증후군 코로나바이러스 (MERS-CoV), 중증급성호흡기증후군 코로나바이러스 (SARS-CoV), 중증급성호흡기증후군 코로나바이러스 2 (SARS-CoV-2)를 포함한 인플루엔자 바이러스와 코로나바이러스는 이미 많은 바이러스성 폐렴과 함께 세계적으로 유행하고 있다. 이러한 감염에 대한 더 나은 진단 방법을 개발함으로써, 우리는 더 심각하고 잠재적으로 유행성 질병을 방어할 전략을 세울 수 있다. SERS와 압타머의 이점을 이용하여, SERS 기반의 여러 aptasensor를 인플루엔자 A/H1N1과 SARS-CoV-2와 같은 호흡기 바이러스 진단에 적용하여 개발하였다. 제2장에서는, 인플루엔자 A /H1N1 바이러스 검출을 위한 SERS 기반의 이미징 aptasensor에 대해 연구하였다. SERS 기반의 생체 인식 센서는 인플루엔자 A /H1N1 바이러스 검사에 높은 민감도를 보이고 있으나 신호 재현성을 높여야 한다. 그래서 우리는 여러 개의 핫스팟을 가진 3차원 (3D) 나노-팝콘 플라스마 기판을 제작하였다. 디옥시리보핵산 (DNA) 압타머와 인플루엔자 A/H1N1 바이러스 간의 상호작용을 통해, Cy3-labeled 압타머가 나노 팝콘 기질의 표면에서 방출되어 라만신호가 감소된다. 이에 따라, 인플루엔자 A/H1N1 바이러스의 정량 평가가 이루어졌다. SERS 기반의 A/H1N1 검사의 추정 범위는 97 PFU/mL이며, 추정 검사 시간은 20분이다. 제3장에서는 SARS-CoV-2 용해물을 고감도로 정량 할 수 있는 새로운 SERS 기반 aptasensor 플랫폼을 개발했다. 본 연구에서는 SARS-CoV-2의 유능한 검출을 위한 수용체로 스파이크 단백질 DNA 앱타머를 사용하였다. 이 기술은 15분 이내에 10 PFU/mL 미만의 검출 한계(LOD)로 SARS-CoV-2를 검출할 수 있다. 이 연구의 결과는 검사 한계와 정확성을 현저하게 높일 수 있을 뿐만 아니라 임상 응용 가능성도 가지고 있음을 증명하였다. 제4장에서는 SARS-CoV-2와 인플루엔자 A/H1N1바이러스를 빠르고 정확하게 동시에 진단하고 구별하기 위해 듀얼 모드 SERS 기반의 맞춤형 센서를 개발하였다. 이 경우 라만리포트 분자 (Cy3과 RRX)로 표기된 DNA 압타머가 Au 나노팝콘 기질에 함께 붙어 SARS-CoV-2와 인플루엔자 A/H1N1 바이러스를 선택적으로 결합할 수 있게 된다. 또 내부 표준 라만 리포트 분자 (4-MBA)를 도입해 환경 변화에 따른 측정 오차를 줄였다. SARS-CoV-2나 인플루엔자 A/H1N1 바이러스가 접근하면 해당 DNA 압타머를 선택적으로 분리한다. SERS 기반의 aptasensor를 이용하면 환자가 SARS-CoV-2에 감염됐는지, A/H1N1에 감염됐는지 판별할 수 있다. 따라서 이러한 SERS 기반의 aptasensor는 바이러스 병원체를 탐지하는 새로운 진단 플랫폼으로 개념적으로 간주될 수 있으며, 호흡기 질환을 빠르게 식별하여 전파를 방지할 수 있다.

Johannes Brahms의 <Vier ernste Gesänge Op.121> 분석 연구 -피아노 반주의 분석을 중심으로-

CHEN HAO 삼육대학교 일반대학원 2023 국내박사

요하네스 브람스(Johannes Brahms, 1833-1897)는 낭만주의 음악의 기법과 함께 고전적 형식성을 추구하였으며, 전 생애에 걸쳐 260여곡의 가곡을 작곡했고 많은 독일 민요를 편곡했다. 브람스는 삶과 죽음에 관한 통찰을 통해 구약성경과 신약성경, 성경 외경 등의 내용을 사용하여 <Vier ernste Gesänge Op.121>(4개의 엄숙한 노래)를 완성했다. 브람스의 <Vier ernste Gesänge Op.121>에 포함된 전 4곡 중 제1곡 <Denn es gehet dem Menschen>은 구약성경의 잠언 3장에 나오는 구절을 사용하여 작곡하였고, 제2곡 <Ich wandte mich und sahe an>도 구약성경의 잠언 4장에 나오는 구절이며, 제3곡 <O Tod, Wie bitter bist du>은 성경 외경인 예수 시라크서 41장에 나타나는 구절을 사용했다. 제4곡 <Wenn ich mit Menschen und mit Engelszungen redete>은 신약성경인 고린도전서 13의 내용을 사용하여 작곡되었다. 이 4곡의 가곡에 사용된 성경의 내용을 종합하면, 죽음에 있어서 사람이나 동물이 서로 동등하며 이 세상의 험한 삶을 살아갈 때 신앙을 지키며 서로 사랑하라는 내용이다. 브람스의 <Vier ernste Gesänge Op.121>는 잦은 전조와 반음계적 진행 등 낭만주의적 성향이 나타나고 있으며, 피아노 반주부의 전주는 2마디의 짧은 형태로 나타나거나 생략된다. 각 작품의 주어진 동기는 다양한 방법으로 모방이나 변형이 이루어진다. 동기의 전개 방법으로는 캐논(canon) 기법, 단순 모방(imitation)기법, 동형진행(sequence), 지속 저음(Organ Point) 등 고전적 작곡 성향을 보인다. 화성의 처리에 있어서 주요 3화음, 반음계적 선율진행, 잦은 전조, 감7화음(diminished 7th chord) 등을 사용했다. 주요어; 요하네스 브람스, <Vier ernste Gesänge Op.121>, 신앙, 죽음, 피아노 반주부.

Exploring the Relationship between Institutional environment and Chinese Corporate Value in the Context of Emerging Market : Innovation capability, ESG performance and Digital Transformation

CHEN HAO 강원대학교 대학원 2024 국내석사

Since the 20th century, the economies of emerging markets have rapidly evolved both in size and growth potential. As one of these markets, China has been facing the challenge of transitioning from a labor-intensive to a technology-driven and innovation-driven economy. This transformation depends heavily on the policies of the government, the reform of the market and the international cooperation. But there are significant differences between emerging markets and developed countries, especially in the lack of IPR protection, the imperfect laws, and the widespread interference of the government in economic activities. China, as one of the leading emerging markets, has attracted a lot of attention from scholars due to the influence of its institutional environment on the value of enterprises, especially in view of the prime role played by the government in economic development. Additionally, China's industrial policies shape the business environment and strategic decisions of enterprises in a highly competitive environment. Therefore, it is of paramount importance to understand and analyse how the Chinese institutional environment affects the value of enterprises in order to enhance the competitiveness and realize the sustainable development. In this environment, a stable and predictable institutional environment can provide businesses with a safe operational framework that reduces commercial risks and attracts investment. At the same time, the increasing awareness of ESG (environmental, social and governance), the ability to innovate, and the digital transformation of businesses are key factors for their success. Because ESG practices enhance competitiveness, attract investors, comply with international rules, and help achieve United Nations development objectives. Innovation has the capacity to promote economic growth and competitiveness, to expand the market, to increase productivity and to satisfy the needs of consumers. In addition, the digital transformation improves competitiveness by cutting costs, improving the quality of services and facilitating fast growth.. This paper explores the influence of China's unique institutional environment on the value of enterprises by taking into account the special location of these factors. It also aims to examine how such an environment shapes business operations, with emphasis on the moderating effect of ESG performance and innovation capacity, as well as the mediation effect of digital transformation. The purpose of this paper is to provide theoretical support for the effective strategic decision making in China, as well as to enrich academic discourse in related areas. Based on the data of listed companies in the Chinese A stock market from 2009 to 2022, this paper studies the relationship between the institutional environment, ESG performance, innovation ability, digital transformation and corporate value. Additionally, based on literature review this paper explains the mediating role of ESG performance and innovative ability, as well as the mediation role of digital transformation. The findings also highlight the importance of an enabling institutional environment in Chinese emerging markets to improve the efficiency of the market and the rational allocation of resources, thus enhancing the value of firms. In addition, positive ESG performance, innovation capacity, and digital transformation reinforce these positive effects. As a result, the research recommends that corporate leaders give priority to improving ESG performance, innovative ability, and digital transformation in order to generate long term value. At the same time, to urge policy makers to improve the institutional context of China's emerging markets, and to foster a supportive environment and policy framework for sustainable and value-creating enterprises. It also has important implications for both theory and practice for companies active in new Chinese markets and provides valuable insights for business and political decision makers. 20세기 이후, 신흥 시장의 경제는 규모와 성장 잠재력 모두에서 급속하게 발전해 왔다. 이러한 시장들 중 하나로서, 중국은 노동 집약적인 경제에서 기술 주도적이고 혁신 주도적인 경제로 전환하는 도전에 직면해 왔다. 이러한 변화는 정부의 정책, 시장의 개혁, 그리고 국제 협력에 크게 의존한다. 그러나 신흥 시장과 선진국 사이에는, 특히 지적재산권 보호의 부족, 불완전한 법, 그리고 경제 활동에 대한 정부의 광범위한 간섭에 있어서 중대한 차이가 있다. 중국은 대표적인 신흥 시장 중 하나로, 특히 경제 발전에서 정부의 주요 역할을 고려할 때 기업의 가치에 대한 제도적 환경의 영향으로 학자들의 많은 관심을 받고 있다. 또한 중국의 산업 정책은 고도의 경쟁 환경에서 기업의 경영 환경과 전략적 의사 결정을 형성한다. 따라서 경쟁력을 높이고 지속 가능한 발전을 실현하기 위해서는 중국의 제도적 환경이 기업의 가치에 어떤 영향을 미치는지 이해하고 분석하는 것이 무엇보다 중요하다. 이러한 환경에서 안정적이고 예측 가능한 제도적 환경은 기업에게 상업적 위험을 줄이고 투자를 유치하는 안전한 운영 프레임워크를 제공할 수 있다. 동시에 ESG(환경, 사회 및 지배 구조)에 대한 인식 증가, 혁신 능력 및 기업의 디지털 전환이 성공의 핵심 요소이다. ESG 실적은 경쟁력을 높이고 투자자를 유치하며 국제 규칙을 준수하며 유엔 개발 목표를 달성하는 데 도움을 주기 때문이다. 혁신은 경제 성장과 경쟁력을 촉진하고 시장을 확장하며 생산성을 높이고 소비자의 요구를 충족할 수 있는 능력을 가지고 있다. 또한 디지털 전환은 비용을 절감하고 서비스 품질을 개선하며 빠른 성장을 촉진하여 경쟁력을 향상시킨다. 본 논문은 이러한 요인들의 특수한 위치를 고려하여 중국 고유의 제도적 환경이 기업의 가치에 미치는 영향을 탐색한다. 또한 이러한 환경이 비즈니스 운영을 어떻게 형성하는지 ESG 성과와 혁신 역량의 조절 효과와 디지털 전환의 매개 효과에 중점을 두고 살펴보고자 한다. 본 논문의 목적은 중국에서 효과적인 전략적 의사 결정을 위한 이론적 지원을 제공하고 관련 분야의 학술적 담론을 풍부하게 하는 것이다. 본 논문은 2009년부터 2022년까지 중국 A 주식시장 상장기업 데이터를 바탕으로 제도적 환경, ESG 실적, 혁신 능력, 디지털 전환 및 기업가치 간의 관계를 연구한다.

An Improved Bellman-Ford Algorithm Based on SPFA

Chen, Hao Chonnam National Univ. 2012 국내석사

Structure and phase transformation of nanocrystalline and amorphous alloy thin films

Chen, Hao University of Illinois at Urbana-Champaign 2006 해외박사(DDOD)

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Structure and phase transformation of amorphous and/or nanocrystalline alloys are studied systematically using in-situ electron diffraction (ED) and imaging (high-resolution transmission electron microscopy (HR-TEM) and high-angle annual dark-field scanning transmission electron microscopy (HAADF-STEM)). Nanocrystalline Ag-Cu and Ag-Si thin films and amorphous Cu-Zr thin films of different compositions and thicknesses are synthesized by sputtering deposition and characterized by the morphology and atomic structures. The observations of morphology and structure of AgxCu 1-x thin films show two stages of transformation. The first stage is associated with decomposition from the solid solution phase to two terminal phases (Ag- and Cu-rich), while the second stage is associated with film dewetting and the formation of crystal grains of 101 nm. The first stage transition corresponds to the spinodal decomposition observed earlier in amorphous Ag-Cu alloys. The structure analysis by electron diffraction shows that the initial stage of decomposition is associated with the growth of Cu crystallites in Ag50Cu50 and Ag-rich films and the initial phase separation is also accompanied by strain. The systematic study on the structure and morphology of Ag-Si alloy thin films shows nanocrystalline Ag clusters embedded in amorphous Si matrix for samples of different compositions and thicknesses. Ag cluster sizes increase from 2.5 nm to 3.5 nm as annealing temperature increases from room temperature to 500°C, and the cluster densities decrease accordingly from 2.5x10 4/mum2 to 7.6x103/mum 2. This phenomenon can be explained by grain growth and Ostwald ripening in which big clusters grows bigger at the expense of smaller clusters. The Ag clusters remain relatively stable under annealing; HREM images show crystalline structure at 500°C, which is different from Ag clusters supported on other substrates. TEM observation shows that Ag clusters are embedded inside the amorphous Si matrix, which explains their relative stability at high temperatures.

Numerical Analysis of Isothermal Membrane-based Dehumidification for M-cycle Evaporative Cooler

Chen, hao-nan 부산대학교 대학원 2022 국내석사

In this work, M-cycle evaporative cooler (MEC) integrated with isothermal membrane-based dehumidification (IMBD) has been investigated. The whole system only uses air and water as working flow, instead of any organic refrigerants. A thermodynamic model for the system has been established and a numerical analysis for different inlet air conditions and system operational parameters has been carried out. Relative humidity significantly affects the supply air temperature and COP. And compare the process of dehumidification to evaporative cooling, removing of latent heat account for the most part of cooling capacity. Low inlet air velocity and long IMBD chamber length make cooler supply air temperature with high COP as well. However, for inlet air velocity, the cooling capacity declines because of the small air flux and large scale membrane is limited to manufacturing currently. Finally, the system was evaluated under several summer climate conditions of representative cities around the world. Draw form the result, the system had a promising application prospect.

Toward Efficient Learning Under Structured Dependence: Gaussian Processes and Reinforcement Learning

Chen, Hao The University of Wisconsin - Madison ProQuest Dis 2026 해외박사(DDOD)

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This dissertation studies efficient learning in models with structured dependence. In many large-scale learning problems, efficiency is achieved by exploiting sample-decomposable objectives, for which stochastic optimization is straightforward. However, dependence across observations or recursive dependence through bootstrapped targets can make standard stochastic learning procedures biased, unstable, or computationally prohibitive. This dissertation examines this challenge in two settings: Gaussian process modeling and off-policy reinforcement learning. It shows that structured dependence can be treated directly through theoretical analysis and algorithmic design rather than handled indirectly.The first part of the dissertation studies Gaussian process hyperparameter estimation using minibatch stochastic gradient descent. Because the Gaussian process log marginal likelihood is not separable across observations, minibatch gradients are generally biased, so classical stochastic optimization theory does not directly apply. This dissertation shows that, under suitable kernel conditions, minibatch stochastic gradient methods can still be rigorously analyzed and used for scalable Gaussian process training without modifying the underlying model. These results provide theoretical justification for direct first-order optimization in Gaussian process learning and clarify how statistical and computational considerations interact under dependence.The second part develops a quantile-process framework for sample-efficient off-policy reinforcement learning in continuous control. Off-policy methods improve sample efficiency by reusing replay data, but they also place critic learning in an off-policy bootstrapped regime that can be unstable. Target networks are a standard stabilizing device, but they delay value propagation and can therefore slow learning. To address this stability--efficiency tradeoff, this dissertation represents return distributions as a quantile process. This distributional formulation provides richer supervision, a more expressive value representation, and better-conditioned critic optimization. It supports both distributional off-policy evaluation and control, and further motivates CrossQP and CrossQPac, which combine quantile-process critics with target-network-free stabilization to improve sample efficiency while maintaining stable critic learning.Together, the two parts of the dissertation advance a shared message about efficient stochastic learning under structured dependence. When treated directly through theoretical analysis and algorithmic design, such dependence need not preclude scalable and effective methods in both statistical learning and reinforcement learning.

Cardiac elastography

Chen, Hao The University of Wisconsin - Madison 2009 해외박사(DDOD)

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Cardiac elastography is a promising method for evaluating the functional state of heart muscle. Currently transthoraic echocardiography is used routinely for assessing global and regional myocardial function at rest, with stress echocardiography, used to identify regional ischemia. Clinical diagnosis is based on visually assessed wall motion scores which is semi-quantitative, image quality dependent, and heavily weighted by operator experience. Tissue Doppler imaging, used to assess myocardial muscle velocity, provides quantitative parameters such as longitudinal systolic and diastolic velocity measurements to obtain strain and strain rate data. However, due to narrow-band Doppler phase-shift analysis, they inherit disadvantages associated with Doppler, such as, angle dependence, poor axial resolution, aliasing, and increase in ambiguity of the velocity information. Limitations with Doppler-derived velocity and strain indices have renewed interest in using B-mode based strain and strain rate measurements. B-mode based strain has the advantage of not being directionally limited. Thus, limitations from Doppler imaging, such as an inability to differentiate between active contraction, simple rotation, and translational motion of the heart wall, are no longer as significant. However, B-mode speckle-tracking approaches utilize coarser and significantly less sensitive strain estimations when compared to radiofrequency based cardiac elastography. We utilize radio-frequency signals acquired from clinical ultrasound scanners at frame rates of up to 40/sec. We demonstrate that a frame rate on the order of 10 times the compression frequency is a reasonable compromise to obtain full-field cardiac strain images. A multi-level, hybrid high resolution 2D strain estimation algorithm suited for curvilinear and phased array transducers is proposed to estimate axial, and lateral strains in cardiac tissue. To address the importance of the complex motion of the heart, we have interfaced a finite element based cardiac mechanics model 'Continuity 6' with our 2D ultrasound simulation program, producing simulated 4D (3D + time) RF echo signal data sets in the modeled heart. The impact of lateral shear deformations on strain tensor data, and the use of principal component analysis to improve strain image quality discussed. Principal component analysis provides radial and circumferential strains preferred for clinical diagnosis. Finally, limited evaluations of the techniques proposed in this dissertation are presented on in-vivo cardiac data.

Enriched finite element methods and their application

Chen, Hao Northwestern University 2003 해외박사(DDOD)

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This thesis consists of three parts. In each part, an enrichment method is introduced and numerical examples are provided. The first two parts of this thesis present two techniques to model crack propagations. The modeling of dynamic crack propagation with the finite element method is cumbersome due to the need for mesh regeneration at each time step as crack evolves. This is a formidable task in quasi-static analysis but is much more so in dynamics. The situation is exacerbated in dynamics since remeshing distorts momentum and energy balance. In Chapter 2, an enrichment technique for accurately modeling two dimensional crack propagation within the framework of the finite element method is presented. The technique uses an enrichment basis that spans the asymptotic dynamic crack-tip solution. The enrichment functions and their spatial derivatives are able to exactly reproduce the asymptotic displacement field and strain field for a moving crack. The stress intensity factors for mode I and mode II are taken as additional degrees of freedom. An explicit time integration scheme is used to solve the resulting discrete equations. Numerical simulations of linear elastodynamic problems are reported to demonstrate the accuracy and potential of the technique. In Chapter 3, a methodology is developed for transitioning from a continuum to a discrete discontinuity where the governing partial differential equation loses hyperbolicity. The approach is limited to rate independent materials, so the transition occurs on a set of measure zero. The discrete discontinuity is treated by the extended finite element method (X-FEM) whereby arbitrary discontinuities can be incorporated in the model without remeshing. A new method is developed for the case when the discontinuity ends within an element. The method is applied to several dynamic crack growth problems including the branching of cracks. Numerical examples by using the maximum tensile stress criterion are also reported. The third part deals with discontinuities in the strain field, which are also called weak discontinuities. In Chapter 4, a simple enrichment technique for modeling discontinuities in derivatives that don't conform to the mesh for lower order elements is presented. The technique uses an enrichment basis that contain discontinuities in derivatives. The enrichment shape functions have compact support so the stiffness matrix is sparse. Numerical results for problems in one and two dimensional linear elastostatics show that the method achieves almost the optimal rate of convergence.

Development of a novel double neural network and its applications

Chen, Hao State University of New York at Stony Brook 2015 해외박사(DDOD)

소속기관이 구독 중이 아닌 경우 오후 4시부터 익일 오전 9시까지 원문보기가 가능합니다.

Artificial neural network model is a powerful method that has been widely applied in many different areas. It is essentially a nonlinear statistical model, empirically proved with good prediction accuracy, and has been applied in both regression and classification problems. One challenge in applying artificial neural network models is constructing proper structure adaptive to specific problems. This thesis work is to introduce a novel, double-layered feed-forward neural network (DNN) model with special link patterns. Its applications to genome-wide association studies and stock price prediction in high frequency time scale have been explored. Detecting gene-gene interactions in traditional Genome-wide associate studies (GWAS) is mostly at the SNP level, called SNP-SNP interactions, which ignores the existence of large amount of correlations embedded among nearby SNPs. Popular existing methods with this mechanism, such as multifactor-dimensionality reduction (MDR) and random forests, would usually suffer from redundant interaction tests, due to the correlations between SNPs, and subsequently from less powers. With our new DNN model, we can take advantage of the correlations between SNPs and perform interaction test at the level of SNP blocks. Extensive simulation studies have been conducted to compare our new method with Random Forests. And our simulation results suggest that the DNN model can have higher power than Random Forests in detecting the existence of causal SNPs -- no matter the effect is interactive or marginal. We also have applied the DNN model to financial markets, forecasting changes of stock prices in high frequency. One advantage of our DNN model is that it utilizes correlation information between different stocks, a pattern more commonly observed in high-frequency data but ignored in most existing methods. Our method has been tested on the 100 stocks with largest capital in S&P 500 using 5-minute data, and its performance has been benchmarked with a single layer neural network model and the classical ARMA-GARCH model. The DNN model clearly outperforms to the other models in terms of prediction accuracy and Sharpe ratio. Given the parallelizable scheme of our method with DNN models, it may be capable for designing profitable trading strategies in high frequency time scale.