Thesis (Ph. D.)--Rutgers University, 2010. / "Graduate Program in Computer Science." Includes bibliographical references (p. 93-97).

Thesis (Ph. D.)--University of Texas at Austin, 2005. / Vita. Includes bibliographical references. Also available from UMI.

Thesis (M.S.E.C.E.)--University of Delaware, 2007. / Principal faculty advisor: Dennis W. Prather, Dept. of Electrical and Computer Engineering. Includes bibliographical references.

The development of the monograph of a commonly used Chinese medicine, Lini Semen, for the Hong Kong Chinese Materia Medica Standards (HKCMMS) was recorded. HKCMMS is a set of reliable and internationally recognized standards of Chinese medicine. The monograph of Lini Semen was proposed, endorsed, and under editorial phase for the publication of the 9th volume of HKCMMS. In the proposed HKCMMS monograph of Lini Semen, besides regular content such as macroscopic and microscopic identification, and TLC identification, a HPLC fingerprint method and two assay methods were developed. All methods were well-established and validated. TLC identification: Lini Semen n-hexane extract is identified on a HPTLC Silica gel RP-18 plate, with α-linolenic acid and linoleic acid as the markers. The developing system consisted of acetone, acetic acid and dichloromethane in the ratio of 5:4:2. The spraying reagent was 5% sulphuric acid - ethanol solution and the plate was observed under 366 nm. HPLC fingerprint: Fingerprint of Lini Semen was conducted on a HPLC system with a C8 column. Mobile phase consisted of (A) water and (B) acetonitrile and isopropanol (9:1). Six characteristic peaks, including α-linolenic acid and linoleic acid were detected under 210 nm. Assay: α-Linolenic acid and linoleic acid were detected from Lini Semen n-hexane extract under the same HPLC condition of the fingerprint of Lini Semen. The proposed total content of α-linolenic acid and linoleic acid was not less than 0.56%. Secoisolariciresinol diglucoside (SDG) was detected from hydrolysed Lini Semen extract by HPLC system with a C18 column. Mobile phase consisted of (A) water and (B) acetonitrile. The detection wavelength was 280 nm. The proposed content of SDG was not less than 0.81%. Conclusion: It is the first time to propose a HPLC fingerprint and introduce SDG as a assay marker of Lini Semen in a regional standard monograph. The established methods for TLC identification, HPLC fingerprint, assay of the total content of α-linolenic acid and linoleic acid, as well as assay for SDG were validated with in-house and inter-laboratory comparison to prove that the methods are reliable.

Erythromycin is a macrolide antibiotic used mainly in the treatment of infections caused by gram-positive organisms. Erythromycin base is rap idly degraded in acidic media necessitating the use of structurally modified erythromycin derivatives or acid resistant dosage forms in order to decrease gastric inactivation of the drug. The majority of pharmacokinetic studies to-date have utilized relatively non-specific microbiological assay procedures which are unable to differentiate between concentrations of active erythromycin base and the inactive pro-drug derivatives. A high-performance liquid chromatographic (HPLC) technique is described for the simultaneous determination of erythromycin base and propionate (inactive pro-drug form) in human serum and urine following the oral administration of erythromycin estolate, an acid stable derivative of erythromycin. The method involves a solid-phase extraction step prior to chromatography on a C18 reversed-phase column with coulometric electrochemical detection. Sample handling and storage techniques are presented which minimize hydrolysis of the inactive ester moiety between sample collection and analysis, thereby more accurately reflecting the in vivo situation than in previously published studies. Results from single dose pharmacokinetic studies indicate that only 10-15% of the total erythromycin concentration in vivo is present as the active base component following oral administration of erythromycin estolate. This percentage increases to approximately 25% during multiple dose administration. Novel urinary excretion data are presented which reveal that approximately 40% and 55% of the total erythromycin excreted in urine is excreted as erythromycin base following single and multiple dosages respectively. Computer fitting of mean serum concentration-time data revealed that an open one compartment model with linear first order absorption and elimination best described the absorption and disposition of erythromycin, although poor computer fits for individual data sets were observed. Some evidence of non-linear elimination is presented utilizing both compartmental and non-compartmental pharmacokinetic techniques. Large intra-and inter-personal variability in erythromycin absorption and disposition was experienced which was evaluated in five subjects who each received one 500 mg erythromycin estolate tablet from the same batch, on three separate occasions. In addition. an HPLC method is described for the analysis of "total erythromycin" concentrations following erythromycin estolate administration which involves hydrolysis of the ester component prior to chromatography. as well as an HPLC method utilizing amperometric electrochemical detection capable of monitoring the stability of erythromycin base in stored biological fluids. These methods were uti I ized in various stability studies involving erythromycin base and propionate as well as for the analysis of erythromycin estolate dosage forms.

The structure-property relationships of Ultra-High Performance Concrete (UHPC) were quantified using imaging techniques to characterize the multiscale hierarchical heterogeneities and the mechanical properties. Through image analysis the average size, percent area, nearest neighbor distance, and relative number density of each inclusion type was determined and then used to create Representative Volume Element (RVE) cubes for use in Finite Element (FE) analysis. Three different size scale RVEs at the mesoscale were found to best represent the material: the largest length scale (35 mm side length) included steel fibers, the middle length scale (0.54 mm side length) included large voids and silica sand grains, and the smallest length scale (0.04 mm side length) included small voids and unhydrated cement grains. By using three length scales of mesoscale FE modeling, the bridge of information to the macroscale cementitious material model is more physically based.

The new generations of multi-core chipset architectures achieve unprecedented levels of computational power while respecting physical and economical constraints. The cost of this power is bewildering program complexity. Atmospheric modeling is a grand-challenge problem that could make good use of these architectures if they were more accessible to the average programmer. To that end, software tools and programming methodologies that greatly simplify the acceleration of atmospheric modeling and simulation with emerging multi-core technologies are developed. A general model is developed to simulate atmospheric chemical transport and atmospheric chemical kinetics. The Cell Broadband Engine Architecture (CBEA), General Purpose Graphics Processing Units (GPGPUs), and homogeneous multi-core processors (e.g. Intel Quad-core Xeon) are introduced. These architectures are used in case studies of transport modeling and kinetics modeling and demonstrate per-kernel speedups as high as 40x. A general analysis and code generation tool for chemical kinetics called "KPPA" is developed. KPPA generates highly tuned C, Fortran, or Matlab code that uses every layer of heterogeneous parallelism in the CBEA, GPGPU, and homogeneous multi-core architectures. A scalable method for simulating chemical transport is also developed. The Weather Research and Forecasting Model with Chemistry (WRF-Chem) is accelerated with these methods with good results: real forecasts of air quality are generated for the Eastern United States 65% faster than the state-of-the-art models. / Ph. D.

No description available.

No description available.

Embedded systems based on FPGAs frequently incorporate soft processors. The prevalence of soft processors in embedded systems is due to their flexibility and adaptability to the application. However, soft processors provide moderate performance compared to hard cores and custom logic, hence faster performing soft processors are desirable. Many soft processor architectures have been studied in the past including Vector processors and VLIWs. These architectures focus on regular applications in which it is possible to extract data and/or instruction level parallelism offline. However, applications with irregular parallelism only benefit marginally from such architectures. Targeting such applications, we investigate superscalar, out-of-order, and Runahead execution on FPGAs. Although these architectures have been investigated in the ASIC world, they have not been studied thoroughly for FPGA implementations. We start by investigating the challenges of implementing a typical inorder pipeline on FPGAs and propose effective solutions to shorten the processor critical path. We then show that superscalar processing is undesirable on FPGAs as it leads to low clock frequency and high area cost due to wide datapaths. Accordingly, we focus on investigating and proposing FPGA-friendly OoO and Runahead soft processors. We propose FPGA-friendly alternatives for various mechanisms and components used in OoO execution. We introduce CFC, a novel copy-free checkpointing which exploits FPGA block RAMs for fast and dense storage. Using CFC, we propose an FPGA-friendly register renamer and investigate the design and implementation of instruction schedulers on FPGAs. We then investigate Runahead execution and introduce NCOR, an FPGA-friendly non-blocking cache tailored for FPGAs. NCOR removes CAM-based structures used in conventional designs and achieves the high clock frequency of 278 MHz. Finally, we introduce SPREX, a complete Runahead soft core incorporating CFC and NCOR. Compared to Nios~II, SPREX provides as much as 38% higher performance for applications with irregular data-level parallelism with minimal area overhead.