Object-oriented DBMS requirements

The requirements on an object-oriented DBMS for management of information in a large, complex enterprise are presented. These requirements aid in the achievement of an environment characterized by data sharing, open architectures, application and data portability, and assurance of data integrity. They were defined from the point of view of a user of the DBMS; therefore they describe the expected functionality of the DBMS and do not specify the method of implementation to achieve this functionality. They encompass requirements on the data model, query and data manipulation languages, the system architecure, interfaces to the system, change management, and transaction management.     

Rate of ammonia oxidation in a synthetic saline wastewater by a nitrifying mixed‐culture

Most of the kinetic studies on nitrification have been performed in diluted salts medium. In this work, the ammonia oxidation rate (AOR) was determined by respirometry at different ammonia (0.01 and 33.5 mg N‐NH₃ L^?1), nitrite (0–450 mg N‐NO₂^? L^?1) and nitrate (0 and 275 mg N‐NO₃^? L^?1) concentrations in a saline medium at 30 °C and pH 7.5. Sodium azide was used to uncouple the ammonia and nitrite oxidation, so as to measure independently the AOR. It was determined that ammonia causes substrate inhibition and that nitrite and nitrate exhibit product inhibition upon the AOR. The effects of ammonia, nitrite and nitrate were represented by the Andrews equation (maximal ammonia oxidation rate, r_AOMAX, = 43.2 [mg N‐NH₃ (g VSS_AO h)^?1]; half saturation constant, K_SAO, = 0.11 mg N‐NH₃ L^?1; inhibition constant K_IAO, = 7.65 mg N‐NH₃ L^?1), by the non‐competitive inhibition model (inhibition constant, K_INI, = 176 mg N‐NO₂^? L^?1) and by the partially competitive inhibition model (inhibition constant, K_INA, = 3.3 mg N‐NO₃^? L^?1; α factor = 0.24), respectively. The r_AOMAX value is smaller, and the K_SAO value larger, than the values reported in diluted salts medium; the K_IAO value is comparable to those reported. Process simulations with the kinetic model in batch nitrifying reactors showed that the inhibitory effects of nitrite and nitrate are significant for initial ammonia concentrations larger than 100 mg N‐NH₄⁺ L^?1. Copyright © 2005 Society of Chemical Industry     

A Bayesian propensity score adjustment for latent variable modeling and MCMC algorithm

The estimation of the differences among groups in observational studies is frequently inaccurate owing to a bias caused by differences in the distributions of covariates. In order to estimate the average treatment effects when the treatment variable is binary, Rosenbaum and Rubin [1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41-55] proposed an adjustment method for pre-treatment variables using propensity scores. Imbens [2000. The role of the propensity score in estimating dose-response functions. Biometrika 87, 706-710] extended the propensity score methodology for estimation of average treatment effects with multivalued treatments.However, these studies focused only on estimating the marginal mean structure. In many substantive sciences such as the biological and social sciences, a general estimation method is required to deal with more complex analyses other than regression, such as testing group differences on latent variables. For latent variable models, the EM algorithm or the traditional Monte Carlo methods are necessary. However, in propensity score adjustment, these methods cannot be used because the full distribution is not specified.In this paper, we propose a quasi-Bayesian estimation method for general parametric models that integrate out the distributions of covariates using propensity scores. Although the proposed Bayes estimates are shown to be consistent, they can be calculated by existing Markov chain Monte Carlo methods such as Gibbs sampler. The proposed method is useful to estimate parameters in latent variable models, while the previous methods were unable to provide valid estimates for complex models such as latent variable models.We also illustrated the procedure using the data obtained from the US National Longitudinal Survey of Children and Youth (NLSY1979-2002) for estimating the effect of maternal smoking during pregnancy on the development of the child's cognitive functioning.     

Age of acquisition: Its neural and computational mechanisms.

The acquisition of new skills over a life span is a remarkable human ability. This ability, however, is constrained by age of acquisition (AoA); that is, the age at which learning occurs significantly affects the outcome. This is most clearly reflected in domains such as language, music, and athletics. This article provides a perspective on the neural and computational mechanisms underlying AoA in language acquisition. The authors show how AoA modulates both monolingual lexical processing and bilingual language acquisition. They consider the conditions under which syntactic processing and semantic processing may be differentially sensitive to AoA effects in second-language acquisition. The authors conclude that AoA effects are pervasive and that the neural and computational mechanisms underlying learning and sensorimotor integration provide a general account of these effects. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Measuring consistency in TTL-based caches

We propose a general modeling framework to evaluate the performance of cache consistency algorithms. In addition to the usual hit rate, we introduce the hit^* rate as a consistency measure, which captures the fraction of non-stale downloads from the cache. We apply these ideas to the analysis of the fixed TTL consistency algorithm in the presence of network delays. The hit and hit^* rates are evaluated when requests and updates are modeled by renewal processes. Classical results on the renewal function lead to various bounds.     

Modeling for local dynamic behaviors of phenol biodegradation in bubble columns

A coupled three-dimensional (3-D) model, combining hydrodynamics with biochemical reactions, was developed to simulate the local transient flow patterns and the dynamic behaviors of cell growth and phenol biodegradation by yeast Candida tropicalis in the bubble-column bioreactor, using the computational fluid dynamic (CFD) method. In order to validate this proposed model effectively, the validation of the local hydrodynamic characteristics of the gas-mineral salt solution (gas-liquid) two-phase system, with the phenol concentration of 1200 mg/L, and with the absence of cells, was performed in a square-sectioned bubble column bioreactor using the LDA system and conductivity probe. Furthermore, the validation of phenol biodegradation behaviors by yeast Candida tropicalis at different initial concentrations of phenol and cell was also carried out in the above bubble-column bioreactor. The results indicated that the model simulations had a satisfying agreement with the experimental data. Finally, the local instantaneous flow and phenol biodegradation features including gas holdup, gas velocity, liquid velocity, cell concentration and phenol concentration inside the bioreactor were successfully predicted in different-scale bubble columns by the proposed model. © 2006 American Institute of Chemical Engineers AIChE J, 2006     

Optimal design of gating systems by gradient search methods

A numerical optimization technique based on gradient-search is applied to obtain an optimal design of a typical gating system used for the gravity process to produce aluminum parts. This represents a novel application of coupling nonlinear optimization techniques with a foundry process simulator, and it is motivated by the fact that a scientifically guided search for better designs based on techniques that take into account the mathematical structure of the problem is preferred to commonly found trial-and-error approaches. The simulator applies the finite volume method and the VOF algorithm for CFD analysis. The direct gradient optimization algorithm, sequential quadratic programming (SQP), was used to solve both a 2D and a 3D gating system design problems using two design variables. The results clearly show the effectiveness of the proposed approach for finding high quality castings when compared with current industry practices.     

膏盐岩地层地质特征与录井技术

石油勘探实践和研究结果表明，许多油气藏的形成都与膏盐岩地层有关，也常与膏盐岩地层伴生，成功钻穿膏盐岩地层是钻井难题之一，录井技术成为钻进安全监测、膏盐岩地层准确预报的重要手段。针对上述情况，介绍了膏盐岩地层的主要类型、沉积模式以及与油气的关系，归纳总结了膏盐岩地层的录井技术经验，包括：在随钻录井过程中熟悉地质设计与邻井资料；在没有预测钻遇膏盐岩地层的情况下，通过钻井液中Cl^-的变化判定是否钻遇膏盐岩地层；预计钻遇膏盐岩地层时，使用饱和盐水钻井液；钻遇膏盐岩地层后观察盐岩是否以岩屑形式返出；同时，把握膏盐岩地层的岩性、岩性组合、层位、顶底界和厚度是非常重要的。通过S1井实例分析，论述了在膏盐岩地层中钻进时，防卡、防漏是录井人员监测和预报的重点。     

Battery-Aware Routing for Streaming Data Transmissions in Wireless Sensor Networks

Recent technological advances have made it possible to support long lifetime and large volume streaming data transmissions in sensor networks. A major challenge is to maximize the lifetime of battery-powered sensors to support such transmissions. Battery, as the power provider of the sensors, therefore emerges as the key factor for achieving high performance in such applications. Recent study in battery technology reveals that the behavior of battery discharging is more complex than we used to think. Battery powered sensors might waste a huge amount of energy if we do not carefully schedule and budget their discharging. In this paper we study the effect of battery behavior on routing for streaming data transmissions in wireless sensor networks. We first give an on-line computable energy model to mathematically model battery discharge behavior. We show that the model can capture and describe battery behavior accurately at low computational complexity and thus is suitable for on-line battery capacity computation. Based on this battery model we then present a battery-aware routing (BAR) protocol to schedule the routing in wireless sensor networks. The routing protocol is sensitive to the battery status of routing nodes and avoids energy loss. We use the battery data from actual sensors to evaluate the performance of our protocol. The results show that the battery-aware protocol proposed in this paper performs well and can save a significant amount of energy compared to existing routing protocols for streaming data transmissions. Network lifetime is also prolonged with maximum data throughput. As far as we know, this is the first work considering battery-awareness with an accurate analytical on-line computable battery model in sensor network routing. We believe the battery model can be used to explore other energy efficient schemes for wireless networks as well.