An integrated air-POM syngas/dimethyl ether process from natural gas

A Cu-Zn-Al methanol catalyst combined with HZSM-5 was used for dimethyl ether (DME) synthesis from a syngas containing nitrogen, which was produced by air-partial oxidation of methane (air-POM). Air-POM occurred at 850 degreesC, 0.8 MPa, CH4/air/H2O/CO2 ratio of 1/2.4/0.8/0.4 over a Ni-based catalyst modified by magnesia and lanthanum oxide with 96% CH4 conversion and constantly gave syngas with a H-2/CO ratio of 2/1 during a period of 450 h. The obtained N-2-containing syngas was used directly for DME synthesis. About 90% CO per-pass conversion, 78% DME selectivity and 70% DME yield could be achieved during 450 h stability testing under the pressure of 5.0 MPa. the temperature of 240 degreesC and the space velocity of 1000 h(-1). (C) 2002 Elsevier Science B. V. All rights reserved.

Wet air oxidation of nitrobenzene enhanced by phenol

Simultaneous nitrobenzene and phenol wet air oxidation was investigated in a stainless autoclave at temperature range of 180-220 ° C and 1.0 MPa oxygen partial pressure. Compared with the single oxidation of nitrobenzene under the same conditions, the presence of phenol in the reaction media greatly improved the removal efficiency of nitrobenzene. The effect of temperature on the reaction was studied. Phenol was considered as a type of initiator in the nitrobenzene oxidation. © 2004 Elsevier Ltd. All rights reserved.

Volcanic air pollution and mortality in France 1783?1784

Grattan J.P., Rabartin, R., Self, S. & Thordarson, Th. 2005. Volcanic air pollution and mortality in France 1783-84. Comptes Rendu Geosciences. 641-651 This item is available in both English and French in the PDF file.

An econometric analysis of international air travel demand in Saudi Arabia

Abed, S. Y., Ba-Fail, A. O., & Jasimuddin, S. (2001). An econometric analysis of international air travel demand in Saudi Arabia. Journal of Air Transport Management, 7(3), 143-148 RAE2008

The Gospel Worship Service Presents Unique Challenges for Chaplains: A Primer on Leading a Gospel Service in the Air Force

This project examines the challenges military chaplains face when leading Gospel services in the United States Air Force in both domestic and deployed locations. It argues that some chaplains assigned to Gospel services do not have the ministry skills set to lead them effectively. Through quantitative and qualitative research methods involving surveys of 30 military chaplains, lay leaders and parishioners, and follow-up interviews to explore critical issues identified by leaders and congregants alike, this project develops a Gospel service manual. This instructional primer outlines the historical evolution of the Gospel service and addresses its integral elements of worship and challenges that chaplains need to understand to meet the worship needs of multicultural and ecumenical military congregations.

CONFIGR: A Vision-Based Model for Long-Range Figure Completion

CONFIGR (CONtour FIgure GRound) is a computational model based on principles of biological vision that completes sparse and noisy image figures. Within an integrated vision/recognition system, CONFIGR posits an initial recognition stage which identifies figure pixels from spatially local input information. The resulting, and typically incomplete, figure is fed back to the “early vision” stage for long-range completion via filling-in. The reconstructed image is then re-presented to the recognition system for global functions such as object recognition. In the CONFIGR algorithm, the smallest independent image unit is the visible pixel, whose size defines a computational spatial scale. Once pixel size is fixed, the entire algorithm is fully determined, with no additional parameter choices. Multi-scale simulations illustrate the vision/recognition system. Open-source CONFIGR code is available online, but all examples can be derived analytically, and the design principles applied at each step are transparent. The model balances filling-in as figure against complementary filling-in as ground, which blocks spurious figure completions. Lobe computations occur on a subpixel spatial scale. Originally designed to fill-in missing contours in an incomplete image such as a dashed line, the same CONFIGR system connects and segments sparse dots, and unifies occluded objects from pieces locally identified as figure in the initial recognition stage. The model self-scales its completion distances, filling-in across gaps of any length, where unimpeded, while limiting connections among dense image-figure pixel groups that already have intrinsic form. Long-range image completion promises to play an important role in adaptive processors that reconstruct images from highly compressed video and still camera images.

Fusion Artmap: A Neural Network Architecture for Multi-Channel Data Fusion and Classification

Fusion ARTMAP is a self-organizing neural network architecture for multi-channel, or multi-sensor, data fusion. Single-channel Fusion ARTMAP is functionally equivalent to Fuzzy ART during unsupervised learning and to Fuzzy ARTMAP during supervised learning. The network has a symmetric organization such that each channel can be dynamically configured to serve as either a data input or a teaching input to the system. An ART module forms a compressed recognition code within each channel. These codes, in turn, become inputs to a single ART system that organizes the global recognition code. When a predictive error occurs, a process called paraellel match tracking simultaneously raises vigilances in multiple ART modules until reset is triggered in one of them. Parallel match tracking hereby resets only that portion of the recognition code with the poorest match, or minimum predictive confidence. This internally controlled selective reset process is a type of credit assignment that creates a parsimoniously connected learned network. Fusion ARTMAP's multi-channel coding is illustrated by simulations of the Quadruped Mammal database.