An accurate and practical buffer allocation model for the buffer cache based on marginal gains

A new dynamic buffer allocation strategy based on the notion of marginal gains is presented for the buffer cache that is used by the operating system to store frequently accessed disk blocks in main memory, and the performance of the proposed strategy is compared with those of previous allocation strategies. In the proposed strategy, marginal gain values are predicted by exploiting functions that approximate the expected number of buffer hits per unit time. Experimental results from both trace-driven simulation and an actual implementation in the FreeBSD operating system show that the proposed strategy accurately predicts the marginal gain values for various workloads resulting in significantly improved buffer hit ratios.     

Profile-Based Linguistic Uniformity as a Generic Method for Comparing Language Varieties

In this text we present``profile-based linguistic uniformity', a methoddesigned to compare language varieties on thebasis of a wide range of potentiallyheterogeneous linguistic variables. In manyrespects a parallel can be drawn with currentmethods in dialectometry (for an overview, see,Nerbonne and Heeringa, 2001; Heeringa, Nerbonneand Kleiweg, 2002): in both casesdissimilarities between varieties on the basisof individual variables are summarized inglobal dissimilarities, and a series oflanguage varieties are subsequently clusteredor charted using multivariate techniques suchas cluster analysis or multidimensionalscaling. This global similarity between themethods makes it possible to compare them andto investigate the implications of notabledifferences. In this text we specifically focuson, and defend one characteristic of ourmethodology, its profile-based nature.     

Hybrid Approach for Addressing Uncertainty in Risk Assessments

Parameter uncertainty is a major aspect of the model-based estimation of the risk of human exposure to pollutants. The Monte Carlo method, which applies probability theory to address model parameter uncertainty, relies on a statistical representation of available information. In recent years, other uncertainty theories have been proposed as alternative approaches to address model parameter uncertainty in situations where available information is insufficient to identify statistically representative probability distributions, due in particular to data scarcity. The simplest such theory is possibility theory, which uses so-called fuzzy numbers to represent model parameter uncertainty. In practice, it may occur that certain model parameters can be reasonably represented by probability distributions, because there are sufficient data available to substantiate such distributions by statistical analysis, while others are better represented by fuzzy numbers (due to data scarcity). The question then arises as to how these two modes of representation of model parameter uncertainty can be combined for the purpose of estimating the risk of exposure. This paper proposes an approach (termed a hybrid approach) which combines Monte Carlo random sampling of probability distribution functions with fuzzy calculus. The approach is applied to a real case of estimation of human exposure, via vegetable consumption, to cadmium present in the surficial soils of an industrial site located in the north of France. The application illustrates the potential of the proposed approach, which allows the uncertainty affecting model parameters to be represented in a way that is consistent with the information at hand. Also, because the hybrid approach takes advantage of the “rich” information provided by probability distributions, while retaining the conservative character of fuzzy calculus, it is believed to hold value in terms of a “reasonable” application of the precautionary principle.     

Proactive resource allocation for asynchronous real-time distributed systems in the presence of processor failures

We present two proactive resource allocation algorithms, RBA^*-FT and OBA-FT, for fault-tolerant asynchronous real-time distributed systems. The algorithms consider an application model where task timeliness is specified by Jensen's benefit functions and the anticipated application workload during future time intervals is described by adaptation functions. In addition, we assume that reliability functions of processors are available a priori. Given these models, our objective is to maximize aggregate task benefit and minimize aggregate missed deadline ratio in the presence of processor failures. Since determining the optimal solution is computationally intractable, the algorithms heuristically compute sub-optimal resource allocations, but in polynomial time. Experimental results reveal that RBA^*-FT and OBA-FT outperform their non-fault-tolerant counterparts in the presence of processor failures. Furthermore, RBA^*-FT performs better than OBA-FT, although OBA-FT incurs better worst-case and amortized computational costs. Finally, we observe that both algorithms robustly withstand errors in the estimation of anticipated failures.     

Allocation of Scarce Water Resources Using Deficit Irrigation in Rotational Systems

On irrigation schemes with rotational irrigation systems in semiarid tropics, the existing rules for water allocation are based on applying a fixed depth of water with every irrigation irrespective of the crops, their growth stages, and soils on which these crops are grown. However, when water resources are scarce, it is necessary to allocate water optimally to different crops grown in the irrigation scheme taking account of different soils in the command area. Allocating water optimally may lead to applying less water to crops than is needed to obtain the maximum yield. In this paper, a three stage approach is proposed for allocating water from a reservoir optimally based on a deficit irrigation approach, using a simulation-optimization model. The allocation results with a deficit irrigation approach are compared for a single crop (wheat) in an irrigation scheme in India, first with full irrigation (irrigation to fill the root zone to field capacity) and second with the existing rule. The full irrigation with a small irrigation interval was equivalent to adequate irrigation (no stress to the crop). It is found that practicing deficit irrigation enables the irrigated area and the total crop production in the irrigation scheme used for the case study to be increased by about 30–45% and 20–40%, respectively, over the existing rule and by 50 and 45%, respectively, over the adequate irrigation. Allocation of resources also varied with soil types.     

铅锌选矿厂废水回用研究

简述了铅锌选厂废水回用小型试验及工业试验情况，在基本上不改变工艺结构基础上改变药剂制度达到废水回用的目的，使选矿废水回用率达到80％，实现尾矿库废水零排放。     

Probabilistic Strength Estimates and Reliability of Damaged Parallel Wire Cables

Cable reliability analysis involves the combined evaluation of cable capacity and cable load in a probabilistic manner. Assessment of cable capacity is only possible through visual inspections of the wires, field sampling, laboratory analysis of the degraded wire populations, and analytical techniques. In addition to a brief presentation of cable mechanics and deterministic models that approximate cable strength, this paper discusses inspection methodologies and statistical methods of estimation of the sizes of the degraded wire populations, and wire properties, leading to cable capacities. These capacities are described by probability distributions. The paper also discusses fundamentals of reliability analysis as they apply to bridge cables. Load criteria of present standard specifications (such as AASHTO or other international codes) are not applicable to long-span suspension bridges. The paper discusses criteria of bridge loading and reliability indices for bridge cables. More work is needed in the evaluation of loading for long-span bridges.     

民族特色词语的英汉互译

语言在其发展演变过程中,逐步具有了鲜明的民族文化色彩。阐述了民族特色词语的可译性,并提出了具体的翻译方法。     

EPON媒体接入控制的关键技术研究

文章简要介绍了基于以及太网的无源光网络（EPON）这种新型的接入网技术，将其与基于ATM技术的无源光网络（APON）的优缺点进行了比较，并对其媒体接入控制（MAC）的关键技术测距及上行接入进行了讨论，初步提出了光网络单元（ONU）的软、硬件实现方案。     

基于Gibbs模型的提高图像分辨率方法

在提高图像分辨率过程中,选择合适的成像模型是获得高分辨率图像的一个关键因素。提出一种基于最大后验概率、采用Gibbs成像模型,并利用多帧序列图像重构高分辨率图像的方法,充分利用序列图像之间的累加信息,从而快速地获得较其他方法更优的高分辨率图像。