Comprehensive Study of Steam Reforming of Methane in Membrane Reactors

A 2D model and heat transfer mechanism are proposed to analyze and study oxidative steam reforming of methane (OSRM) in a membrane reactor. The model describes mass and thermal dispersions for gas and solid phases. It also accounts for transport through the membrane. The effects of operating parameters on methane conversion and H 2 yield are analyzed. The parameters considered are the bed temperature (800-1100 K), molar oxygen-to-carbon ratio (0.0-0.5), and steam-to-carbon ratio (1-4). The results show that our model prevents overestimation and provides valuable additional information about temperature and concentration gradients in membrane reactor which is not available in a simple one-dimensional approach. Simulation results show that large temperature and concentration gradients cannot be avoided. The particle properties and the bed diameter have a considerable effect on the extent of gas mixing. Effective gas mixing coefficient also increases with increasing gas and solid velocity. In membrane reactor, simulation results show that mixing which depends on operational and design parameters has a strong effect on the hydrogen conversion. Also, the removal of hydrogen with membranes breaks equilibrium barrier leading to efficient production of hydrogen, reduced reactor size, and tube lengths. The model can be used in real-time simulation of industrial reactors for control and optimization purposes

Deformable Registration of a Preoperative 3D Liver Volume to a Laparoscopy Image Using Contour and Shading Cues

The deformable registration of a preoperative organ volume to an intraoperative laparoscopy image is required to achieve augmented reality in laparoscopy. This is an extremely challenging objective for the liver. This is because the preoperative volume is textureless, and the liver is deformed and only partially visible in the laparoscopy image. We solve this problem by modeling the preoperative volume as a Neo-Hookean elastic model, which we evolve under shading and contour cues. The contour cues combine the organ’s silhouette and a few curvilinear anatomical landmarks. The problem is difficult because the shading cue is highly nonconvex and the contour cues give curve-level (and not point-level) correspondences. We propose a convergent alternating projections algorithm, which achieves a $$4$$ registration error

New supersymmetric solutions in N=2 matter coupled AdS(3) supergravities

We construct new 1/2 supersymmetric solutions in D = 3, N = 2, matter coupled, U(1) gauged supergravities and study some of their properties. We do this by employing a quite general supersymmetry breaking condition, from which we also redrive some of the already known solutions. Among the new solutions, we have an explicit non-topological soliton for the non-compact sigma model, a locally flat solution for the compact sigma model and a string-like solution for both types of sigma models. The last one is smooth for the compact scalar manifold

Contextually Appropriate Reference Generation

We describe a system for contextually appropriate anaphor and pronoun generation for Turkish. It uses Binding Theory and Centering Theory to model local and nonlocal reference. We describe the rules for Turkish, and their computational treatment. A cascaded method for anaphor and pronoun generation is proposed for handling pro-drop and discourse constraints on pronominalization. The system has been tested as a standalone nominal expression generator and also as a reference planning component of a transfer-based MT system

Spatio-temporal correlation: theory and applications for wireless sensor networks

Wireless Sensor Networks (WSN) are characterized by the dense deployment of sensor nodes that continuously observe physical phenomenon. Due to high density in the network topology, sensor observations are highly correlated in the space domain. Furthermore, the nature of the physical phenomenon constitutes the temporal correlation between each consecutive observation of a sensor node. These spatial and temporal correlations along with the collaborative nature of the WSN bring significant potential advantages for the development of efficient communication protocols well-suited for the WSN paradigm. In this paper, several key elements are investigated to capture and exploit the correlation in the WSN for the realization of advanced efficient communication protocols. A theoretical framework is developed to model the spatial and temporal correlations in WSN. The objective of this framework is to enable the development of efficient communication protocols which exploit these advantageous intrinsic features of the WSN paradigm. Based on this framework, possible approaches are discussed to exploit spatial and temporal correlation for efficient medium access and reliable event transport in WSN, respectively

The 2001 GMTK-based SPINE ASR system

This paper provides a detailed description of the University of Washington automatic speech recognition (ASR) system for the 2001 DARPA SPeech In Noisy Environments (SPINE) task. Our system makes heavy use of the graphical modeling toolkit (GMTK), a general purpose graphical modeling-based ASR system that allows arbitrary parameter tying, flexible deterministic and stochastic dependencies between variables, and a generalized maximum likelihood parameter estimation algorithm. In our SPINE system, GMTK was used for acoustic model training whereas feature extraction, speaker adaptation, and first-pass decoding were performed by HTK. Our integrated GMTK/HTK system demonstrates the relative merits provided by each tool. Novel aspects of our SPINE system include the capturing of correlations among feature vectors via a globally-shared factored sparse inverse covariance matrix and generalized EM training. 1

Impact of histological subtype on the prognosis of patients undergoing surgery for colon cancer

Background: The effect of the histological subtype on the prognosis of patients undergoing surgery for colon cancer (CC) is not completely understood. Methods: The Surveillance, Epidemiology, and End Results (SEER) 2004\u20132014 database was used to compare the long-term outcomes of patients undergoing colon resection for classical adenocarcinoma (CA), mucinous adenocarcinoma (MUC), and signet-cell adenocarcinoma (SC). Results: A total of 153 317 (89%) patients had CA, 16 660 (10%) MUC while 1810 (1%) patients had SC subtype. Patients with MUC and SC more frequently had a poorly differentiated CC and were more likely to present with advanced disease compared with CA patients (P < 0.001). Patients with CA had a 5-year OS of 62% versus 55% and 34% for patients with MUC and SC subtypes, respectively (P = 0.001). On multivariable analysis, site of cancer, tumor grade, and TNM stage were associated with prognosis (all P < 0.001). After controlling for these risk factors, patients with MUC (HR, 1.09, P < 0.001) and SC (HR, 1.47, P < 0.001) had a roughly 10% and 50% increased hazard of death, respectively, compared with CA patients. Conclusions: MUC and SC are distinct subtypes of CC associated with a worse prognosis. These data can help inform discussion about prognosis and possibly direct adjuvant management