1,720,968 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Recognition of the Upper Limb���s Motion Intention Using Electroencephalogram and Machine Learning Techniques
Get PDFThe electroencephalogram (EEG) signals are a measurable brain electrical activity. EEG signals can be used to detect and classify motion intention of any voluntary action. Successful detection of EEG signals and classification of motion intention is crucial in Brain-Computer Interface (BCI) applications. BCI can be used for upper limb rehabilitation through the use of prosthetics or exoskeletons. In this project, a method is proposed to distinguish three motions, including moving an arm forward, grabbing an object, and moving an arm upwards as well as the rest position, a total of 4 different tasks. The data is collected using ENOBIO 8 system with seven electrodes. Four time-domain features extracted from the data include the mean absolute value, zero crossing, waveform length, and slope sign change. The k-Nearest Neighbor (k-NN) algorithm is used to classify the four classes. This study investigates various window sizes and different numbers of neighbors to achieve a higher classification accuracy. Using a grid search approach, it was determined that a window size of 1500 ms and a number of neighbors of produced the highest classification accuracy. The classification accuracy was 85.6 ��2.38%, while previous studies were only able to achieve classification accuracies between 60% and 78%. This result proves that varying the window size and the number of neighbors profoundly influence classification accuracy of motion intention; therefore, improving rehabilitation techniques for people with minimal arm movement and muscular dystrophy. The classified signals can be used for further biomedical research and be utilized to expand the growing biomedical research field in Qatar that relates to Brain-Computer Interface (BCI) technology
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Utilizing modeling tools to design a reactor and a catalyst for dry reforming of methane
Get PDFDry reforming of methane (DRM) is a catalytic reaction in which two greenhouse gases (CO2 and CH4) are converted to synthesis gas (a mixture of CO and H2), an important precursor to produce various chemical products. DRM is highly attractive due to its ability to convert greenhouse gases; however more research is required to address its process challenges: (a) high energy requirement, (b) low synthesis gas quality, and (c) catalyst deactivation due to carbon formation. Nickel (Ni) catalyst is widely used for methane reforming and thus suitable for DRM as well. However, it is prone to carbon formation due to reactions like methane decomposition and Boudouard reaction causing deactivation. A novel bimetallic nickel-copper (Ni-Cu) catalyst was previously developed in our research group at an atomistic scale using density functional theory (DFT) approach to address the Ni catalyst���s carbon formation challenge. The Ni-Cu catalyst provides significant carbon resistance and superior stability compared to the conventional Ni catalyst. The catalyst���s performance was proven and validated experimentally in our laboratory���s state-of-the-art bench-top reactor.
The scope of this thesis is to explore the scalability of the novel Ni-Cu catalyst using a mathematical modeling approach. The approach comprises of utilizing an existing one-dimensional (1-D) pseudo-homogeneous reactor bed model supported by lumped kinetics of a network of complex reactions that take place during DRM. This model was updated by including accountability of carbon formation and advancing further to account for detailed transport properties. The kinetics of Ni to Ni-Cu catalyst were scaled using a novel approach utilizing DFT and results in providing predictions for the bulk-scale kinetics performance in terms of carbon formation rates.
The experimental validation of the model was first tested using a conventional monoatomic Ni catalyst then later extended to predict the performance of the Ni-Cu catalyst. The 1-D model yielded results that match within an error margin of 5% with experimental data especially on CH4 conversions. The developed 1-D model serves as a tool to predict the performance of the Ni-Cu catalyst at various reactor scales and to conduct future optimization and process intensification studies for DRM process by maximizing feed conversions
Improved Source-Reconstruction Through the Exploitation of Dose-Response Models
Get PDFThe emergency actions following an accidental or intentional release of a hazardous material (HazMat) are strongly influenced by what is known of the HazMat event and its evolution (e.g. mass flowrate of the release, location of the release, and the duration of the release). Thus, the availability of such information in a timely and accurate manner is of great importance for emergency crews both on-site and in the areas in the proximity of the site. This information aids in predicting the fate of the release and mitigating the possible threats imposed by it on human health.
Characterizing the source of an atmospheric contaminant is typically regarded as an inverse problem, where one has to infer the source characteristics of the released HazMat from concentration or deposition measurements. The inverse solution is obtained by combining atmospheric dispersion models and concentration measurements in an optimal way, where dispersion models are used to produce concentration predictions in space and time using as input initial guesses of the source information. Furthermore, estimating the source parameters needed for atmospheric transport and dispersion modeling requires the application of deterministic and stochastic inversion techniques, such as the Bayesian frames employing Monte Carlo methods.
Locating the source and determining its release rate based on downwind concentration measurements, however, is only viable for some cases like industrial accidents, where measurement data from monitoring stations are typically obtained on-site. For other release cases, such as transport accidents or malevolent attacks, there is a lack of input data (i.e. immediate concentration measurements). Hence, there is a necessity to develop new approaches for real cases where this information is probably not available to the required extent or at all. This work presents the development, application and assessment of computational algorithms used in reconstructing the source characteristics following an accidental release of an airborne hazard. A promising methodology is the utilization of the resulting health symptoms from exposure as indirect input for emergency response systems. In this work, a total of six scenarios were constructed and analyzed in terms of their ability to reconstruct the source rate in addition to the source location. The results revealed that the source term information can be identified with good agreement with the true source parameters when using the new scheme. However, the complexity of the information used as input was reflected on the minimum requirement of input data needed to reconstruct the source term. The results also revealed the necessity to explore other sophisticated sampling and inversion techniques
Integration of Engine and TWC Models through Developing an Exhaust Manifold Model for Predicting Motor Vehicle Emissions during Drive Cycles
Get PDFEstimating emissions of an internal combustion engine depends on the interactions between different vehicle components, such as the three-way catalyst
(TWC) and the exhaust manifold. This thesis focuses on developing a transient integrated low-order model for multi-cylinder spark-ignition engines to predict the formation, transfer, and reduction of significant pollutants NO, CO, CO2, O2, and UHC (unburned hydrocarbons). This was achieved through integrating four models: a torque-speed model, an exhaust manifold model, a combustion model, and a TWC model.
The combustion process is modeled as a continuously stirred-tank reactor (CSTR) and assumes a simplified gasoline formulation using a two-lumped reaction mode. The exhaust manifold is modeled based on the conservation of energy, mass, and momentum equations and presented by a set of three first-order hyperbolic partial differential equations (PDEs). The exhaust gas properties at the manifold are obtained by using the Lax-Wendorff numerical scheme to solve the PDEs. The gas velocity, temperature, and density are estimated throughout the manifold length and operating times. The emission reduction in TWC is predicted by a lumped analysis of reductants and oxidants and accounts for oxygen. It is assumed that the reaction only occurs at the wash coat, and symmetry simplifies the TWC from a three-dimensional to one-dimension model.
The integrated model is coupled with a torque-speed model to convert a predefined vehicle speed profile to the engine's torque and RPM by considering the main powertrain components: flywheel, gearbox, differential drive-axle, and wheel size. The emission prediction with the torque-speed model minimizes extensive emission mapping techniques and optimizes the various vehicle system dependencies
The variations between experimental data and theoretical models are mainly due to their difference in spark timing, fuel composition, heating value, and the exact molecular weight of fuel. The fuel data is imported from literature, where quantifying fuel composition accurately would improve the model's prediction accuracy and improve the overall combined model emissions prediction for SI (spark ignition) engines. The integrated model presented here sites the ground for developing and testing customized driving cycles for future emission regulation purposes
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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