PROGRESS IN THE MODELLING OF AIR FLOW PATTERNS IN TIMBER KILNS

Progress in modelling air flow patterns in timber kilns using Computational Fluid Dynamics is reviewed in this work. These simulations are intended to predict the distribution of the flow in the fillet spaces between boards in a hydraulic model of a timber kiln. Here, the flow regime between the boards is transitional between laminar and turbulent flow, with Reynolds numbers of the order of 5000. Running the simulation as a transient calculation has shown few problems with convergence issues, reaching a mass residual of 0.2% of the total inflow after 40-100 iterations per time step for time steps of 0.01 s. Grid sensitivity studies have shown that non-uniform grids are necessary because of the sudden changes in flow cross section, and the flow simulations are insensitive to grid refinement for non-uniform grids with more than 300,000 cells. The best agreement between the experimentally-measured flow distributions between fillet spaces and those predicted by the simulation have been achieved for (effective) bulk viscosities between the laminar viscosity for water and ten times that value. This change in viscosity is not very large (less than an order of magnitude), given that effective turbulent viscosities are typically several orders of magnitude greater than laminar ones. This result is consistent with the transitional flows here.     

民族传统体育项目引入高校体育课的研究

在高校开展少数民族传统优秀体育项目教学,不仅能使少数民族传统体育走向规范化、科学化,较好地展现学生的运动能力,培养民族文化情感,激发民族热情,更重要的是还能提高学生的身心健康和综合素质,有利于对民族传统体育的继承、发展和广泛地传播。     

新型城镇化下欠发达地区传统文化保护研究——以张家口市段家堡村为例

新型城镇化背景下欠发达地区传统文化保护的关键取决于其能否在保护文化资本和环境资本的基础上,实现经济资本的提升、文化价值观念及生活方式的转变.通过对张家口市下花园区段家堡村传统文化的调查研究,提出转变观念,主动保护、挖掘和丰富欠发达地区传统文化特色,提升旅游开发优势,带动地区经济发展,实现新型城镇化的同时更好地保护传统文化.     

中国传统性伦理思想对大学生性教育的影响

性伦理思想对我国当代大学生性教育具有十分重要的意义,通过对中国传统性伦理思想的研究分析,探索其对大学生性教育产生的主要影响,为进一步推进当代大学生的性教育提供合理性依据。     

Mining partial periodic correlations in time series

Recently, periodic pattern mining from time series data has been studied extensively. However, an interesting type of periodic pattern, called partial periodic (PP) correlation in this paper, has not been investigated. An example of PP correlation is that power consumption is high either on Monday or Tuesday but not on both days. In general, a PP correlation is a set of offsets within a particular period such that the data at these offsets are correlated with a certain user-desired strength. In the above example, the period is a week (7 days), and each day of the week is an offset of the period. PP correlations can provide insightful knowledge about the time series and can be used for predicting future values. This paper introduces an algorithm to mine time series for PP correlations based on the principal component analysis (PCA) method. Specifically, given a period, the algorithm maps the time series data to data points in a multidimensional space, where the dimensions correspond to the offsets within the period. A PP correlation is then equivalent to correlation of data when projected to a subset of the dimensions. The algorithm discovers, with one sequential scan of data, all those PP correlations (called minimum PP correlations) that are not unions of some other PP correlations. Experiments using both real and synthetic data sets show that the PCA-based algorithm is highly efficient and effective in finding the minimum PP correlations. Zhen He is a lecturer in the Department of Computer Science at La Trobe University. His main research areas are database systems optimization, time series mining, wireless sensor networks, and XML information retrieval. Prior to joining La Trobe University, he worked as a postdoctoral research associate in the University of Vermont. He holds Bachelors, Honors and Ph.D degrees in Computer Science from the Australian National University. X. Sean Wang received his Ph.D degree in Computer Science from the University of Southern California in 1992. He is currently the Dorothean Chair Professor in Computer Science at the University of Vermont. He has published widely in the general area of databases and information security, and was a recipient of the US National Science Foundation Research Initiation and CAREER awards. His research interests include database systems, information security, data mining, and sensor data processing. Byung Suk Lee is associate professor of Computer Science at the University of Vermont. His main research areas are database systems, data modeling, and information retrieval. He held positions in industry and academia: Gold Star Electric, Bell Communications Research, Datacom Global Communications, University of St. Thomas, and currently University of Vermont. He was also a visiting professor at Dartmouth College and a participating guest at Lawrence Livermore National Laboratory. He served on international conferences as a program committee member, a publicity chair, and a special session organizer, and also on US federal funding proposal review panel. He holds a BS degree from Seoul National University, MS from Korea Advanced Institute of Science and Technology, and Ph.D from Stanford University. Alan C. H. Ling is an assistant professor at Department of Computer Science in University of Vermont. His research interests include combinatorial design theory, coding theory, sequence designs, and applications of design theory.     

浅谈在艺术设计教育中加强中国传统文化学习的必要性

近年来,正当许多发达国家努力发展本土文化的时候,我国在很多方面特别是艺术设计教育领域却忽视了具有5000年历史的中国传统文化。传统文化在现代设计教育中缺失的问题,表面上是教育体系、教育方法的问题,实际是教育理念的问题。面对西方文化巨大冲击,完善我国现代设计教育体系,确保民族文化根脉的活力,是我们必须面对、也必须尽快解决的问题。     

中国传统符号的演变——门

人类学会构筑房屋并为自己的进出留下一个进出方便的口,门就产生了。门是一个极为抽象的形态,一个变化万千的符号,这个古老的符号——门的形态变化一直在随着科技的发展而演变,无论是古人还是后人都在其中精心地演绎着中国的传统民族文化。     

基于传统文化艺术资源整合基础上的山东区域特色动漫设计研究

山东区域的传统文化艺术资源主要包括:民间传说、寓言故事、文学作品、文物古迹、传统艺术等内容。     

Conservative and aggressive rough SVR modeling

Support vector regression provides an alternative to the neural networks in modeling non-linear real-world patterns. Rough values, with a lower and upper bound, are needed whenever the variables under consideration cannot be represented by a single value. This paper describes two approaches for the modeling of rough values with support vector regression (SVR). One approach, by attempting to ensure that the predicted high value is not greater than the upper bound and that the predicted low value is not less than the lower bound, is conservative in nature. On the contrary, we also propose an aggressive approach seeking a predicted high which is not less than the upper bound and a predicted low which is not greater than the lower bound. The proposal is shown to use ?-insensitivity to provide a more flexible version of lower and upper possibilistic regression models. The usefulness of our work is realized by modeling the rough pattern of a stock market index, and can be taken advantage of by conservative and aggressive traders.     

用高新技术改造传统产业

研究鞍山钢铁集团公司(以下简称鞍钢)的基本状况，并对改造前和改造后的鞍钢状况做了对比分析，明确鞍钢改革的方向。论证了鞍钢用高新技术改造传统产业的策略，指出鞍钢要生存和发展，必须走体制创新、技术创新和管理创新的道路，实现企业结构优化，增强与国际钢铁企业竞争的实力，从而为大型国有企业走出困境提供了一些有益的经验。