国家科技支撑计划预、决算及财务验收申请报告编制之探讨

国家科技支撑计划在财务方面要求课题承担单位严格按照经费管理法规,在课题申报时做预算书、每年年初做上年度课题经费财务决算报告、课题终结前做财务验收申请报告。针对国家科技支撑计划经费管理、预算书、课题经费财务决算报告、财务验收申请报告编制概况及要求和甘肃省医学科学研究院编制情况,阐述经费管理、报表编制的对策建议、心得体会及应注意的事项。

新《预算法》背景下科研事业单位预、决算数据保持一致的探讨

新《预算法》对各级政府、各部门、各单位的预、决算编制、监督、法律责任等具体事项都做了明确的规定,但部门预、决算数据仍存在非正常预算调整因素的差异率。笔者结合自身的工作体会和调研结果,对科研事业单位预、决算数据产生差异率的原因进行分析,探讨解决问题的办法,希望能促进完善科研事业单位预、决算管理制度。

浅析公路建设投资、计量支付的有效运作

公路是现代经济社会发展的重要基础设施,是现代经济社会快速运作的先行,是市场机制作用于人类经济行为的首要物质前提。没有一个现代化的公路交通运输体系,就不可能形成一个完善的市场经济。而交通运输发达的前提,则在于建设一套完整的交通基础设施。发达的交通基础设施建设需要投入巨额资金。因此,解决公路建设投资和公路建设投资的有效运作,将成为发展公路交通基础设施的关键。

Prediction method of rock burst proneness based on rough set and genetic algorithm

A new method based on rough set theory and genetic algorithm was proposedto predict the rock burst proneness. Nine influencing factors were first selected, and then,the decision table was set up. Attributes were reduced by genetic algorithm. Rough setwas used to extract the simplified decision rules of rock burst proneness. Taking the practical engineering for example, the rock burst proneness was evaluated and predicted bydecision rules. Comparing the prediction results with the actual results, it shows that theproposed method is feasible and effective.

An Empirical Test of Optimism Bias in Capital Budgeting Decisions

Behavioral finance is a field that is scrutinizing the adequacy of traditional financial theories using insights from the disciplines of psychology and sociology. Many studies within its realm test the stock market behaviors, and behavioral phenomena are still to be tested in the area of corporate finance. This study aims to contribute to the behavioral corporate finance literature by a research in one of the psychological phenomena affecting the decision makers＇ abilities to reach conclusions rationally. In this study, it is aimed to investigate one of the biases, namely, the optimism bias in corporate capital budgeting decisions. Optimism in decision making can be associated with estimating lower costs and higher revenues. Thus, by assessing the forecasts of decision makers, the existence of optimism in their decisions is tried to be seen. This study aims at contributing to the literature in that it is conducted in an emerging country like Turkey.

DiagData： A Tool for Generation of Fuzzy Inference System

In this paper, it described the architecture of a tool called DiagData. This tool aims to use a large amount of data and information in the field of plant disease diagnostic to generate a disease predictive system. In this approach, techniques of data mining are used to extract knowledge from existing data. The data is extracted in the form of rules that are used in the development of a predictive intelligent system. Currently, the specification of these rules is built by an expert or data mining. When data mining on a large database is used, the number of generated rules is very complex too. The main goal of this work is minimize the rule generation time. The proposed tool, called DiagData, extracts knowledge automatically or semi-automatically from a database and uses it to build an intelligent system for disease prediction. In this work, the decision tree learning algorithm was used to generate the rules. A toolbox called Fuzzygen was used to generate a prediction system from rules generated by decision tree algorithm. The language used to implement this software was Java. The DiagData has been used in diseases prediction and diagnosis systems and in the validation of economic and environmental indicators in agricultural production systems. The validation process involved measurements and comparisons of the time spent to enter the rules by an expert with the time used to insert the same rules with the proposed tool. Thus, the tool was successfully validated, providing a reduction of time.

Influences of misprediction costs on solar flare prediction

The mispredictive costs of flaring and non-flaring samples are different for different applications of solar flare prediction.Hence,solar flare prediction is considered a cost sensitive problem.A cost sensitive solar flare prediction model is built by modifying the basic decision tree algorithm.Inconsistency rate with the exhaustive search strategy is used to determine the optimal combination of magnetic field parameters in an active region.These selected parameters are applied as the inputs of the solar flare prediction model.The performance of the cost sensitive solar flare prediction model is evaluated for the different thresholds of solar flares.It is found that more flaring samples are correctly predicted and more non-flaring samples are wrongly predicted with the increase of the cost for wrongly predicting flaring samples as non-flaring samples,and the larger cost of wrongly predicting flaring samples as non-flaring samples is required for the higher threshold of solar flares.This can be considered as the guide line for choosing proper cost to meet the requirements in different applications.

Dominant edge direction based fast intra mode decision in the H.264/AVC encoder

The H.264/AVC video coding standard uses an intra prediction mode with 4×4 and 16×16 blocks for luma and 8×8 blocks for chroma. This standard uses the rate distortion optimization (RDO) method to determine the best coding mode based on the compression performance and video quality. This method offers a large improvement in coding efficiency compared to other compression standards, but the computational complexity is greater due to the various intra prediction modes. This paper proposes a fast intra mode decision algorithm for real-time encoding of H.264/AVC based on the dominant edge direction (DED). The DED is extracted using pixel value summation and subtraction in the horizontal and vertical directions. By using the DED, three modes instead of nine are chosen for RDO calculation to decide on the best mode in the 4×4 luma block. For the 16×16 luma and the 8×8 chroma, only two modes are chosen instead of four. Experimental results show that the entire encoding time saving of the proposed algorithm is about 67% compared to the full intra search method with negligible loss of quality.