利用反预报方法从截面预言近滴线丰质子核的结合能

在Origin等数据处理软件中,当实验数据较少时,自由参数的不同初始化设置会导致较大的结果差异,这为物理结果的确定带来较大不确定性.通过最小二乘法分析了345MeV/u ~(78) Kr+~9Be反应中产生的丰质子同位素的截面和结合能,并得到线性回归方程.通过回归方程,利用结合能预报部分丰质子核素的截面,以及通过实验截面对近质子滴线核素的结合能进行反预报测量.这对于近质子滴线的丰质子核素实验测量具有较好的借鉴意义.     

线性回归分析与能源需求预测

结合实例介绍了回归模型方法在能源预测中的应用，具体地讨论了最简单、最基本的直线回归模型结构及参数估计方法，对于其他一些曲线回归模型则可通过变量代换转化为直线回归模型。     

Finding finer functions for partially characterized proteins by protein-protein interaction networks

Based on high-throughput data, numerous algorithms have been designed to find functions of novel proteins. However, the effectiveness of such algorithms is currently limited by some fundamental factors, including (1) the low a-priori probability of novel proteins participating in a detailed function; (2) the huge false data present in high-throughput datasets; (3) the incomplete data coverage of functional classes; (4) the abundant but heterogeneous negative samples for training the algorithms; and (5) the lack of detailed functional knowledge for training algorithms. Here, for partially characterized proteins, we suggest an approach to finding their finer functions based on protein interaction sub-networks or gene expression patterns, defined in function-specific subspaces. The proposed approach can lessen the above-mentioned problems by properly defining the prediction range and functionally filtering the noisy data, and thus can efficiently find proteins’ novel functions. For thousands of yeast and human proteins partially characterized, it is able to reliably find their finer functions (e.g., the translational functions) with more than 90% precision. The predicted finer functions are highly valuable both for guiding the follow-up wet-lab validation and for providing the necessary data for training algorithms to learn other proteins.     

Predictability and model selection in the context of ARCH models

Most of the methods used in the ARCH literature for selecting the appropriate model are based on evaluating the ability of the models to describe the data. An alternative model selection approach is examined based on the evaluation of the predictability of the models in terms of standardized prediction errors. Copyright © 2005 John Wiley & Sons, Ltd.     

Non-Parametric Regression Methods

The nature of the financial time series is complex, continuous interchange of stochastic and deterministic regimes. Therefore, it is difficult to forecast with parametric techniques. Instead of parametric models, we propose three techniques and compare with each other. Neural networks and support vector regression (SVR) are two universally approximators. They are data-driven non parametric models. ARCH/GARCH models are also investigated. Our assumption is that the future value of Istanbul Stock Exchange 100 index daily return depends on the financial indicators although there is no known parametric model to explain this relationship. This relationship comes from the technical analysis. Comparison shows that the multi layer perceptron networks overperform the SVR and time series model (GARCH).     

斜坡变形破坏的预测及防护

通过对斜坡变形破坏和影响因素的研究，探讨工程建设中斜坡变形破坏的预测及防护。     

临界等温线的推算──几个立方型状态方程的比较

对ＰＴ方程等几个立方型状态方程描述氩的临界等温线的能力作比较研究。在临界压力以下区域．ＰＴ方程、ＨＳ－ＳＲＫ方程和ＳＲＫ方程推算精度较好，ＡＡＤ＜０．４％。在临界压力以上区域，所讨论的五个方程都不能提供具有合理精度的描述。     

A new model for the life prediction of GH4133 under TMF conditions

Thermal mechanical cyclic strain tests were carried out under in-phase and out-of-phase conditions on a Nickel-base Superalloy GH4133 in the temperature range of 571-823℃. Based on analyzing the present models of TMF (thermal mechanical fatigue) life prediction, a new model for predicting nickel-base superalloy TMF lifetime was proposed.TMF life of superalloy GH4133 was calculated accurately based on the new model. Experimental TMF life has been compared with the calculatedresults and all results fall in the scatter band of 1.5. The calculating results show that the new model is not only simple, but also precise. This model will play great roles in life prediction of the metal materials and the engineering components subjected to non-isothermal service conditions.     

应用加权列联表分析预测汛期（4─9月）洪涝趋势

本文根据汉寿县１９６０─１９８８年洪涝灾害资料，应用加权列联表分析法，与前期气候因子建立洪涝趋势预测模式。最后对１９８９─１９９０年洪涝试报，取得了满意的效果。     

燃煤循环流化床燃烧脱硫的模型预测

燃煤循环流化床燃烧脱硫的模型预测池涌，岑可法，倪明江Ｐ·Ｂａｓｕ（浙江大学能源系杭州３１００２７）关键词：循环流化床，燃烧，脱硫，模型预测一、引言循环流化床锅炉在过去十几年中得到了广泛的应用，但与此同时由于循环流化床内流体动力特性的高度复杂使得数学模...