Signature Sequence of Intersection Curve of Two Quadrics for Exact Morphological Classification

We present an efficient method for classifying the morphology of the intersection curve of two quadrics (QSIC) in PR3, 3D real projective space; here, the term morphology is used in a broad sense to mean the shape, topological, and algebraic properties of a QSIC, including singularity, reducibility, the number of connected components, and the degree of each irreducible component, etc. There are in total 35 different QSIC morphologies with non-degenerate quadric pencils. For each of these 35 QSIC morphologies, through a detailed study of the eigenvalue curve and the index function jump we establish a characterizing algebraic condition expressed in terms of the Segre characteristics and the signature sequence of a quadric pencil. We show how to compute a signature sequence with rational arithmetic so as to determine the morphology of the intersection curve of any two given quadrics. Two immediate applications of our results are the robust topological classification of QSIC in computing B-rep surface representation in solid modeling and the derivation of algebraic conditions for collision detection of quadric primitives

Towards Efficient Path Query on Social Network with Hybrid RDF Management

The scalability and exibility of Resource Description Framework(RDF) model make it ideally suited for representing online social networks(OSN). One basic operation in OSN is to find chains of relations,such as k-Hop friends. Property path query in SPARQL can express this type of operation, but its implementation suffers from performance problem considering the ever growing data size and complexity of OSN.In this paper, we present a main memory/disk based hybrid RDF data management framework for efficient property path query. In this hybrid framework, we realize an efficient in-memory algebra operator for property path query using graph traversal, and estimate the cost of this operator to cooperate with existing cost-based optimization. Experiments on benchmark and real dataset demonstrated that our approach can achieve a good tradeoff between data load expense and online query performance

Does Applying Deep Learning in Financial Sentiment Analysis Lead to Better Classification Performance?

Using a unique data set from Seeking Alpha, we compare the deep learning approach with traditional machine learning approaches in classifying financial text. We apply the long short-term memory (LSTM) as the deep learning method and Naive Bayes, SVM, Logistic Regression, XGBoost as the traditional machine learning approaches. The results suggest that the LSTM model outperforms the conventional machine learning methods on all metrics. Based on the tSNE graph, the success of the LSTM model is partially explained as the high-accuracy LSTM model distinguishes between positive and negative important sentiment words while those words are chosen based on SHAP values and also appear in the widely used financial word dictionary, the Loughran-McDonald Dictionary (2011)

Experimental Study of Energy Requirement of CO2 Desorption from Rich Solvent

AbstractAmine scrubbing has been considered to be the most feasible route for CO2 capture. However, the main drawback of this technology is high regeneration energy. A better understanding of energy requirement of CO2 desorption from rich solvent is required. In this study the regeneration energy and its three contributions is examined at various process parameters through experimental work. The regeneration process parameters include rich solvent flow rate, MEA concentration, feeding solvent temperature, rich solvent loading, reboiler temperature and stripper operating pressure. It was found that the regeneration energy was sensitive to those process parameters. The regeneration energy of a mixed MEA/MDEA solvent was also examined. The results show that the regeneration energy can be reduced by using a mixed MEA/MDEA solution

Hybrid theoretical, experimental and numerical study of vibration and buckling of composite shells with scatter in elastic moduli

AbstractHybrid theoretical, experimental and numerical method is proposed for free vibration and buckling of composite shell with unavoidable scatter in elastic moduli. Based on the Goggin’s measurement techniques, the elastic moduli for material T300-QY8911 are measured, and a set of experimental points are obtained. The measurements of elastic moduli are quantified by either (1) the smallest ellipsoid and (2) the smallest four-dimensional uncertainty hyper-rectangle. Then uncertainty propagation in vibration and buckling problems of composite shell by ellipsoidal analysis and interval analysis are, respectively, studied from the theoretical standpoint. Comparison between these analyses is performed numerically

SO2 effect on degradation of MEA and some other amines

AbstractSO2 is the main acidic impurity in flue gas and will affect amine degradation in CO2 capture process. This work introduced SO2/Na2SO3 in various experiment conditions of MEA (monoethanolamine) oxidative degradation and evaluated the SO2 effect on MEA degradation considering both oxidative and thermal degradation. 60ppm SO2 could inhibit MEA oxidative degradation by scavenging oxidative radicals in absorber condition. Higher concentration of SO2 does not enhance the inhibitory effect, but will increase the corrosivity of the solution. NH3 is promoted by sulfite and becomes significant in MEA thermal degradation. Thiosulfate, the disproportionation product of sulfite, is believed to be the catalyst of SN2reaction. Na2SO3 was used to test SO32- effect on thermal degradation of EDA (ethylenediamine), 2-PE (2-piperidineethanol) and PZ/AMP (piperazine/2-amino-2-methyl-1-propanol) solution. Alkyl structure of amines has important effect on the SN2 reactions

Efficient Point based Global Illumination on Intel MIC Architecture

International audiencePoint-Based Global Illumination (PBGI) is a popular rendering method in special effects and motion picture productions. The tree-cut computation is in general the most time consuming part of this algorithm, but it can be formulated for efficient parallel execution, in particular regarding wide-SIMD hardware. In this context, we propose several vectorization schemes, namely single, packet and hybrid, to maximize the utilization of modern CPU architectures. While for the single scheme, 16 nodes from the hierarchy are processed for a single receiver in parallel, the packet scheme handles one node for 16 receivers. These two schemes work well for scenes having smooth geometry and diffuse material. When the scene contains high frequency bumps maps and glossy reflections, we use a hybrid vectorization method. We conduct experiments on an Intel Many Integrated Corearchitecture and report preliminary results on several scenes, showing that up to a 3x speedup can be achieved when compared with non-vectorized execution

Characterization of pore structure and reservoir properties of tight sandstone with CTS, SEM, and HPMI: A case study of the tight oil reservoir in fuyu oil layers of Sanzhao Sag, Songliao basin, NE China

Quantitative characterization of the pore throat structures and reservoir properties of the tight sandstone in Fuyu oil layers of Sanzhao Sag, Songliao Basin, NE China was carried out using cast thin section (CTS), scanning electron microscopy (SEM) and high-pressure mercury intrusion (HPMI) combined with fractal theory. cast thin section and scanning electron microscopy describe the composition, pore filling, cementation, and connectivity of tight sandstones. Six different fractal models, i.e. 2D capillary model, 3D spherical model, 3D capillary model, geometric model, thermodynamic model, and wetting phase model are used to calculate the fractal dimension of pore size distribution using capillary pressure data of high-pressure mercury intrusion, and their correlations with reservoir properties were analyzed. The images of CTS and SEM show that the main lithology of this oil reservoir is tight sandstone, and the pore types are mainly intergranular dissolved pores and intragranular dissolved pores, with uneven pore distribution and poor connectivity. The study of the HPMI data shows that the throat sorting coefficient is the main influencing factor of tight sandstone permeability. The fractal dimensions calculated from the HPMI capillary pressure data using the 3D capillary model and the wetting phase fractal model have the strongest correlations with the reservoir properties compared with the other fractal model. As the fractal dimension increases, the pore throat structures become more heterogenous and reservoir properties become poorer. For the wetting phase fractal model, the calculated fractal dimension is positively correlated with the heterogeneity of the pore throat structure of tight sandstone, which is contrary to the fractal theory and is not suitable for analyzing pore throat structures. The fractal dimensions calculated from the thermodynamic model have no obvious correlations with reservoir properties and cannot quantitatively characterize the heterogeneity of tight sandstone