A RAWLSIAN EVALUATION OF IRRIGATION DISTRIBUTION IN INDIA1

ABSTRACT: This paper critically evaluates the nature of irrigation distribution in India using the Rawlsian criterion of equity in dis. tribution and estimates the performance of different states in India according to the Rawlsian notion of fairness in distribution. It is found that there is considerable inequality across farm-size groups in the distribution of irrigated areas in general and canal irrigated areas in particular, and that there are wide interstate differences in the levels of this inequality. Further, it is found that switching over to a Rawlsian based distribution of canal irrigation will help in reducing the levels of inequality in overall irrigation distribution in all states.     

浅析环境监测QC/QA过程的采样环节

对环境监测布点采样中的QC/QA以及由采样引起的误差来源做了简要分析,并对监测采样过程中QC/QA的操作提出了一些设想。     

上海市TSP-Pb空气质量监测点位优化研究

通过对上海市23个站点的TSP-Pb监测数据的分析,发现TSP与Pb之间具有良好的相关性.因此考虑只用TSP数据对站点进行优化.通过对各站点TSP数据的聚类分析,将所有站点分为6类,然后对包含多个站点的类,将类中各站点与本类均值一同进行再聚类,选取代表站点.最终选取6个站点作为上海市TSP-Pb污染水平的代表站点.优化前后的对比分析表明,优化结果合理,信息损失量小.     

对《超标环境噪声排污费征收标准》的异议

依据声级测量的误差分析，结合实例，对国家现行《超标环境噪声排污费征收标准》提出异议，建议取消１ｄＢ（Ａ）的超标收费规定、舍弃测量的小数值，并定义一个考虑污染范围的评价量以使收费更加合理。     

PH玻璃电极对比观测及误差研究

经过对PH玻璃电极对比观测实验,总结其在日常观测中衰减变化规律,以及PH电极失效前所具有的特征;讨论了对现有PH观测数据进行修正的方法,并编著相关修正程序;分析日常存在的测量误差,提出提高观测精度的措施。     

计算机类学生编程素质的培养

针对计算机类学生在软件编程中出现的问题，本文就格式化编码、界面设计的一致性和清晰性、错误报告的方式与时机、交互二重性等方面进行了细致的介绍，以Visual Basic 6．x为编程语言提出了相应的解决办法，对提高计算机类专业学生的编程素质有较强的指导意义，对其他软件开发或程序设计人员也有较高的借鉴价值。     

马里亚纳海槽玄武岩K-Ar定年及其地质意义

对马里亚纳海槽弧后扩张脊上所采集的玄武岩样品进行了系统的K -Ar定年 ,同时结合研究玄武岩的微量元素以及稀土元素特征后指出 ,马里亚纳海槽的张开及与之伴随发生的玄武岩浆活动自晚中新世以来一直都在进行 ,其主活动期为 2～ 4Ma。海槽玄武岩年龄在空间上具有从南到北逐渐变新的演化规律。据此 ,推断该海槽的扩张演化可能受控于该区内热区的迁移及运动规律     

关于络合滴定的终点误差的研究

终合滴定是化学分析中的一个重要领域，它广泛应用于各生产部门和科学研究中，络合滴定的终点误差（TE）问题是关系到测定结果是否准确可靠性至涉及分析方法本身是否可靠的大问题，通过理论推导和试验验证，提出了除通常采用的计算方法外的几种络各滴定终点误差计算方法，使读者更进一步明确了有关终点误差的实质和计算运用。     

Short-term Model of the Production of Construction Aggregates in Taiwan Based on Artificial Neural Networks

BACKGROUND: Taiwan's geography and limited stock of sandstone have caused sandstone resources to gradually decline to the point of exhaustion after long-term excavation. Moreover, the Taiwanese government has continuously increased the amount of land area near rivers that cannot be excavated to facilitate riverbed remediation and promote conservation of water resources. Accordingly, predicting and managing the annual production of construction aggregates in future construction projects, and dealing appropriately with some thorny problems, for instance, demand that excess supply, excessive excavation, unregulated excavation, and the consequent environmental damage, will significantly affect the efficient use of natural resources in a manner that accords with the national policy of Sustainable Development (SD). METHODS:. This study establishes an empirical model for forecasting the annual production of future construction aggregates using Artificial Neural Networks (ANN), based on 15 relevant socio-economic indicators, such as indicator of annual consumption of cement. A sensitivity analysis is then performed on these indicators. RESULTS AND DISCUSSION: This work applies ANN to estimate the annual production of construction aggregates; the estimates, the verification of the model and the sensitivity analysis are all acceptable. Furthermore, sensitivity analysis results indicate that the annual consumption of cement is the indicator that most strongly influences the production of construction aggregates, as well as whether construction waste can be recycled and steel structures can be used in buildings, helping to reduce the future production of construction aggregates in Taiwan. CONCLUSIONS: The elaborate prediction methodology presented in this study avoids some of the weaknesses or limitations of conventional linear statistics, linear programming or system dynamics. Additionally, the results not only provide a short-term prediction of the production of construction aggregates in Taiwan, but also provide a viable and flexible means of verifying quality certification of the production data of construction aggregates in the future by incorporating those relevant socio-economic indicators. RECOMMENDATIONS AND OUTLOOK: The continuity and quality of the database of relevant indicators used in this study should be closely scrutinized in order to ensure the SD means of exploiting resources.     

Efficiency of composite sampling for estimating a lognormal distribution

In many environmental studies measuring the amount of a contaminant in a sampling unit is expensive. In such cases, composite sampling is often used to reduce data collection cost. However, composite sampling is known to be beneficial for estimating the mean of a population, but not necessarily for estimating the variance or other parameters. As some applications, for example, Monte Carlo risk assessment, require an estimate of the entire distribution, and as the lognormal model is commonly used in environmental risk assessment, in this paper we investigate efficiency of composite sampling for estimating a lognormal distribution. In particular, we examine the magnitude of savings in the number of measurements over simple random sampling, and the nature of its dependence on composite size and the parameters of the distribution utilizing simulation and asymptotic calculations.