H2S optimization for lignite liquefaction

Two lignite samples, Beulah No. 3 and Big Brown No. 1, were liquefied at 420 °C using H₂ and synthesis gas to determine the optimum beneficial amount of H₂S in the batch autoclave reactor. Under the conditions employed, 50–100 psi partial pressure of H₂S, nominally 4–10 wt% of daf lignite, was optimum for both samples. Synthesis gas outperformed H₂ with and without H₂S for the liquefaction of the two coals.     

An Analysis of Scheduling Policies In Multiechelon Production Systems

Results of a simulation study of the economics of frequency of rescheduling Material Requirements Planning (MRP) systems are presented for a single-product, two-stage system in which demand is uncertain. The results indicate that for systems with moderate demand uncertainty, frequent rescheduling to maintain customer service may be uneconomical when compared with the alternative of more stable schedules in conjunction with safety stock. This result arises primarily because the cost of “emergency” production setups which occur when rescheduling is frequent exceeds the cost of safety stock required to “protect” stable schedules.     

Integration of prior knowledge of measurement noise in kernel density classification

Samples can be measured with different precisions and reliabilities in different experiments, or even within the same experiment. These varying levels of measurement noise may deteriorate the performance of a pattern recognition system, if not treated with care. Here we seek to investigate the benefit of incorporating prior knowledge about measurement noise into system construction. We propose a kernel density classifier which integrates such prior knowledge. Instead of using an identical kernel for each sample, we transform the prior knowledge into a distinct kernel for each sample. The integration procedure is straightforward and easy to interpret. In addition, we show how to estimate the diverse measurement noise levels in a real world dataset. Compared to the basic methods, the new kernel density classifier can give a significantly better classification performance. As expected, this improvement is more obvious for small sample size datasets and large number of features.     

The Effects of H2O, CO and CO2 on the H2 Permeance and Surface Characteristics of 1 mm Thick Pd80wt%Cu Membranes

The hydrogen permeance of 1 mm-thick Pd_80wt%Cu foils was measured in the presence of equimolar mixtures of H₂ with CO, CO₂ or H₂O over the temperature and total pressure ranges of 623–1,173 K and 0.62–2.86 MPa, respectively. In all cases, permeance losses at 623 and 738 K were very modest. At higher temperatures, more significant decreases in membrane permeance were observed with the highest reduction of about 50% occurring when macroscopic carbon deposition occurred on the membrane surface during H₂–CO exposure at 908 K. The more worrisome effects of exposure to these gases, however, were the micron-scale surface defects observed at 908 and 1,038 K. Although the 1 mm thick disk membranes retained their mechanical integrity, such defects could cause catastrophic failure of ultra-thin, Pd–Cu membranes (1–5 μm thick) deposited on porous substrates.     

Stochastic molecular descriptors for polymers. 4. Study of complex mixtures with topological indices of mass spectra spiral and star networks: The blood proteome case

The Quantitative Structure-Property Relationships (QSPRs) based on Graph or Network Theory are important for predicting the properties of polymeric systems. In the three previous papers of this series (Polymer 45 (2004) 3845-3853; Polymer 46 (2005) 2791-2798; and Polymer 46 (2005) 6461-6473) we focused on the uses of molecular graph parameters called topological indices (TIs) to link the structure of polymers with their biological properties. However, there has been little effort to extend these TIs to the study of complex mixtures of artificial polymers or biopolymers such as nucleic acids and proteins. In this sense, Blood Proteome (BP) is one of the most important and complex mixtures containing protein polymers. For instance, outcomes obtained by Mass Spectrometry (MS) analysis of BP are very useful for the early detection of diseases and drug-induced toxicities. Here, we use two Spiral and Star Network representations of the MS outcomes and defined a new type of TIs. The new TIs introduced here are the spectral moments (π_k) of the stochastic matrix associated to the Spiral graph and describe non-linear relationships between the different regions of the MS characteristic of BP. We used the MARCH-INSIDE approach to calculate the π_k(SN) of different BP samples and S2SNet to determine several Star graph TIs. In the second step, we develop the corresponding Quantitative Proteome-Property Relationship (QPPR) models using the Linear Discriminant Analysis (LDA). QPPRs are the analogues of QSPRs in the case of complex biopolymer mixtures. Specifically, the new QPPRs derived here may be used to detect drug-induced cardiac toxicities from BP samples. Different Machine Learning classification algorithms were used to fit the QPPRs based on π_k(SN), showing J48 decision tree classifier to have the best performance. These results suggest that the present approach captures important features of the complex biopolymers mixtures and opens new opportunities to the application of the idea supporting classic QSPRs in polymer sciences.     

智能化使汽车更安全

新的特性必须更加智能,在增加价值的同时不会分散驾驶者的注意力和增加事故的风险。     

Results of a User Study on 2D Hurricane Visualization

We present the results from a user study looking at the ability of observers to mentally integrate wind direction and magnitude over a vector field. The data set chosen for the study is an MM5 (PSU/NCAR Mesoscale Model) simulation of Hurricane Lili over the Gulf of Mexico as it approaches the southeastern United States. Nine observers participated in the study. This study investigates the effect of layering on the observer's ability to determine the magnitude and direction of a vector field. We found a tendency for observers to underestimate the magnitude of the vectors and a counter‐clockwise bias when determining the average direction of a vector field. We completed an additional study with two observers to try to uncover the source of the counter‐clockwise bias. These results have direct implications to atmospheric scientists, but may also be able to be applied to other fields that use 2D vector fields.     

A logical approach to program analysis

This paper describesMicroScope, a framework for developing analysis tools for Lisp programs. MicroScope uses a knowledge-intensive approach for program representation and analysis. The analysis tools share a common object oriented program database, and a common Prolog inference engine. The use of Prolog and a declarative representation for programs permits sharing of information, and provides high bandwidth communication between diverse analysis tools. It also supports program specification and debugging activities in the same framework. Extensions to Prolog to support analysis are described, and two tools, theCritic and theExpector, are presented.This work supported in part by Hewlett-Packard Company, the National Science foundation Under Grant Number MCS81-21750 and the Defense Advanced Research Projects Agency under contract number DAAK11-84-K-0017.     

Assessing Environmental Risks: Lessons From Superfund

This essay analyzes the use of risk assessment in Superfund, the controversial American hazardous waste cleanup programme. We argue that risk assessment has been used in three ways in discussing policy options in this programme: as a metric for assessing performance; as a means for resolving conflicts; and as a tool for comparing different environmental initiatives. Use of risk reduction as an analytical tool in European efforts to clean up hazardous waste sites is discussed. We conclude that while risk assessment has potential utility for each of these applications, there are also accompanying technical and political difficulties.     

Influence ofAmaranthus hybridus L. allelochemics on oviposition behavior ofSpodoptera exigua andS. eridania (Lepidoptera: Noctuidae)

Common pigweed,Amaranthus hybridus L., is a favorite host of the beet army worm (BAW),Spodoptera exigua L. Chemicals extracted from homogenized pigweed with distilled water, ethanol, or dichloromethane and sprayed back on pigweed deterred oviposition by the BAW. Similarly, water extracts of frass from conspecific larvae or southern armyworm (SAW) larvae,S. eridania (Cramer), fed pigweed leaves and sprayed back on pigweed plants also deterred BAW oviposition, thus confirming that deterrence was due to plant allelochemics rather than specific compounds associated with the metabolic or excretory products of the larvae. Confirmation of the presence of oviposition-deterring chemicals in pigweed was used to explain a previously observed seasonal displacement of BAW by SAW on pigweed in the field.Mention of a commercial or proprietary product does not constitute an endorsement by the USDA.