Computational methods for traditional Chinese medicine:a survey

Traditional Chinese Medicine (TCM) has been actively researched through various approaches, including computational techniques. A review on basic elements of TCM is provided to illuminate various challenges and progresses in its study using computational methods. Information on various TCM formulations, in particular resources on databases of TCM formulations and their integration to Western medicine, are analyzed in several facets, such as TCM classifications, types of databases, and mining tools. Aspects of computational TCM diagnosis, namely inspection, auscultation, pulse analysis as well as TCM expert systems are reviewed in term of their benefits and drawbacks. Various approaches on exploring relationships among TCM components and finding genes/proteins relating to TCM symptom complex are also studied. This survey provides a summary on the advance of computational approaches for TCM and will be useful for future knowledge discovery in this area. © 2007 Elsevier Ireland Ltd. All rights reserved.

Hydrogen production by steam reforming of DME in a large scale CFB reactor. Part I:computational model and predictions

This study presents a computational fluid dynamic (CFD) study of Dimethyl Ether steam reforming (DME-SR) in a large scale Circulating Fluidized Bed (CFB) reactor. The CFD model is based on Eulerian-Eulerian dispersed flow and solved using commercial software (ANSYS FLUENT). The DME-SR reactions scheme and kinetics in the presence of a bifunctional catalyst of CuO/ZnO/Al2O3+ZSM-5 were incorporated in the model using in-house developed user-defined function. The model was validated by comparing the predictions with experimental data from the literature. The results revealed for the first time detailed CFB reactor hydrodynamics, gas residence time, temperature distribution and product gas composition at a selected operating condition of 300 °C and steam to DME mass ratio of 3 (molar ratio of 7.62). The spatial variation in the gas species concentrations suggests the existence of three distinct reaction zones but limited temperature variations. The DME conversion and hydrogen yield were found to be 87% and 59% respectively, resulting in a product gas consisting of 72 mol% hydrogen. In part II of this study, the model presented here will be used to optimize the reactor design and study the effect of operating conditions on the reactor performance and products.

A CFD study of biomass pyrolysis in a downer reactor equipped with a novel gas-solid separator-II thermochemical performance and products

A Eulerian-Eulerian CFD model was used to investigate the fast pyrolysis of biomass in a downer reactor equipped with a novel gas-solid separation mechanism. The highly endothermic pyrolysis reaction was assumed to be entirely driven by an inert solid heat carrier (sand). A one-step global pyrolysis reaction, along with the equations describing the biomass drying and heat transfer, was implemented in the hydrodynamic model presented in part I of this study (Fuel Processing Technology, V126, 366-382). The predictions of the gas-solid separation efficiency, temperature distribution, residence time and the pyrolysis product yield are presented and discussed. For the operating conditions considered, the devolatilisation efficiency was found to be above 60% and the yield composition in mass fraction was 56.85% bio-oil, 37.87% bio-char and 5.28% non-condensable gas (NCG). This has been found to agree reasonably well with recent relevant published experimental data. The novel gas-solid separation mechanism allowed achieving greater than 99.9% separation efficiency and < 2 s pyrolysis gas residence time. The model has been found to be robust and fast in terms of computational time, thus has the great potential to aid in future design and optimisation of the biomass fast pyrolysis process.

Influence of reaction atmosphere (H2O, N2, H2, CO2, CO) on fluidized-bed fast pyrolysis of biomass using detailed tar vapor chemistry in computational fluid dynamics

Secondary pyrolysis in fluidized bed fast pyrolysis of biomass is the focus of this work. A novel computational fluid dynamics (CFD) model coupled with a comprehensive chemistry scheme (134 species and 4169 reactions, in CHEMKIN format) has been developed to investigate this complex phenomenon. Previous results from a transient three-dimensional model of primary pyrolysis were used for the source terms of primary products in this model. A parametric study of reaction atmospheres (H2O, N2, H2, CO2, CO) has been performed. For the N2 and H2O atmosphere, results of the model compared favorably to experimentally obtained yields after the temperature was adjusted to a value higher than that used in experiments. One notable deviation versus experiments is pyrolytic water yield and yield of higher hydrocarbons. The model suggests a not overly strong impact of the reaction atmosphere. However, both chemical and physical effects were observed. Most notably, effects could be seen on the yield of various compounds, temperature profile throughout the reactor system, residence time, radical concentration, and turbulent intensity. At the investigated temperature (873 K), turbulent intensity appeared to have the strongest influence on liquid yield. With the aid of acceleration techniques, most importantly dimension reduction, chemistry agglomeration, and in-situ tabulation, a converged solution could be obtained within a reasonable time (∼30 h). As such, a new potentially useful method has been suggested for numerical analysis of fast pyrolysis.

An empirical study on uncertainty identification in social media context

Uncertainty text detection is important to many social-media-based applications since more and more users utilize social media platforms (e.g., Twitter, Facebook, etc.) as information source to produce or derive interpretations based on them. However, existing uncertainty cues are ineffective in social media context because of its specific characteristics. In this paper, we propose a variant of annotation scheme for uncertainty identification and construct the first uncertainty corpus based on tweets. We then conduct experiments on the generated tweets corpus to study the effectiveness of different types of features for uncertainty text identification. © 2013 Association for Computational Linguistics.

Computational model of bladder tissue based on its measured optical properties

Urinary bladder diseases are a common problem throughout the world and often difficult to accurately diagnose. Furthermore, they pose a heavy financial burden on health services. Urinary bladder tissue from male pigs was spectrophotometrically measured and the resulting data used to calculate the absorption, transmission, and reflectance parameters, along with the derived coefficients of scattering and absorption. These were employed to create a "generic" computational bladder model based on optical properties, simulating the propagation of photons through the tissue at different wavelengths. Using the Monte-Carlo method and fluorescence spectra of UV and blue excited wavelength, diagnostically important biomarkers were modeled. Additionally, the multifunctional noninvasive diagnostics system "LAKK-M" was used to gather fluorescence data to further provide essential comparisons. The ultimate goal of the study was to successfully simulate the effects of varying excited radiation wavelengths on bladder tissue to determine the effectiveness of photonics diagnostic devices. With increased accuracy, this model could be used to reliably aid in differentiating healthy and pathological tissues within the bladder and potentially other hollow organs.

Exploiting heterogeneous scientific literature networks to combat ranking bias:evidence from the computational linguistics area

It is important to help researchers find valuable papers from a large literature collection. To this end, many graph-based ranking algorithms have been proposed. However, most of these algorithms suffer from the problem of ranking bias. Ranking bias hurts the usefulness of a ranking algorithm because it returns a ranking list with an undesirable time distribution. This paper is a focused study on how to alleviate ranking bias by leveraging the heterogeneous network structure of the literature collection. We propose a new graph-based ranking algorithm, MutualRank, that integrates mutual reinforcement relationships among networks of papers, researchers, and venues to achieve a more synthetic, accurate, and less-biased ranking than previous methods. MutualRank provides a unified model that involves both intra- and inter-network information for ranking papers, researchers, and venues simultaneously. We use the ACL Anthology Network as the benchmark data set and construct the gold standard from computer linguistics course websites of well-known universities and two well-known textbooks. The experimental results show that MutualRank greatly outperforms the state-of-the-art competitors, including PageRank, HITS, CoRank, Future Rank, and P-Rank, in ranking papers in both improving ranking effectiveness and alleviating ranking bias. Rankings of researchers and venues by MutualRank are also quite reasonable.

A study of the impact of persuasive argumentation in political debates

Persuasive communication is the process of shaping, reinforcing and changing others' responses. In political debates, speakers express their views towards the debated topics by choosing both the content of their discourse and the argumentation process. In this work we study the use of semantic frames for modelling argumentation in speakers' discourse. We investigate the impact of a speaker's argumentation style and their effect in influencing an audience in supporting their candidature. We model the influence index of each candidate based on their relative standings in the polls released prior to the debate and present a system which ranks speakers in terms of their relative influence using a combination of content and persuasive argumentation features. Our results show that although content alone is predictive of a speaker's influence rank, persuasive argumentation also affects such indices.

A computational model of hydrogen production by steam reforming of dimethyl ether in a large scale CFB reactor. Part II:parametric analysis

This study presents a computational parametric analysis of DME steam reforming in a large scale Circulating Fluidized Bed (CFB) reactor. The Computational Fluid Dynamic (CFD) model used, which is based on Eulerian-Eulerian dispersed flow, has been developed and validated in Part I of this study [1]. The effect of the reactor inlet configuration, gas residence time, inlet temperature and steam to DME ratio on the overall reactor performance and products have all been investigated. The results have shown that the use of double sided solid feeding system remarkable improvement in the flow uniformity, but with limited effect on the reactions and products. The temperature has been found to play a dominant role in increasing the DME conversion and the hydrogen yield. According to the parametric analysis, it is recommended to run the CFB reactor at around 300 °C inlet temperature, 5.5 steam to DME molar ratio, 4 s gas residence time and 37,104 ml gcat -1 h-1 space velocity. At these conditions, the DME conversion and hydrogen molar concentration in the product gas were both found to be around 80%.