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。     

电站锅炉过热器泄露原因及典型案例分析

近几年,电站锅炉中过热器泄露事件时有发生.本论文分析了过热器泄露的典型案例,讨论了影响锅炉过热器泄露的主要因素,并针对机组状况提出了行之有效的防治措施,提高了机组的经济性和安全性.     

借助大型活动提升渭南市旅游发展研究

近年来,全国各地成功举办了各种大型活动,这些大型活动在城市旅游发展中起到了积极的促进作用。文章通过分析大型活动对渭南市旅游发展带来的影响,提出了借助大型活动提升渭南市旅游发展的相关对策:借助渭南市的经济地位,积极举办省内、市内的各项大型活动;借助当地著名资源,举办各项专题活动;借助区位优势,依托西安举办的各种大型活动来提升当地旅游发展。     

非制度化利益表达产生的原因及其治理路径

理论界多认为民众失衡的社会心理是非制度化利益表达产生的原因,忽略了单位制解体后的组织缺位或越位是导致民众非制度化利益表达方式产生的重要因素.这是因为：一方面,单位组织解体后利益表达主体没有可以依赖的政治或社会组织,缺乏社会资本支撑;另一方面,利益分化导致的利益结构失衡使利益表达主体产生了消极政治心理,影响着其政治行为.因此,非制度化利益表达的治理路径是：强化已有利益表达渠道的效能,使民众拥有组织归属感;拓宽利益表达渠道,化解民众消极政治心理.     

概率学中圆上n点共半圆问题的解决及应用

通过圆上三点共半圆概率的求解,解决圆上点共半圆问题的简便计算方法,并进而推导出圆上点同落于一段确定圆弧内概率的计算公式。以此为数学模型,介绍其在几何、统计及质量检验管理等方面的应用。     

柴河水库汛期可能遭遇的极端和突发水文事件的初始识别及对应的初步预案

本文对柴河水库在汛期可能遭遇的极端突发水文事件的类型作了初级预测,并设计了初步的预报调度方面的对应预案。     

Solving the Problem of Forbidden States in Discrete Event Systems: A Novel Systematic Method for Reducing the Number of Control Places

This paper deals with the problem of forbidden states in Discrete Event Systems modelled by non‐safe Petri Nets. To avoid these states, some Generalized Mutual Exclusion Constraints can be assigned to them. These constraints limit the weight sum of tokens in some places and can be enforced on the system using control places. When the number of these constraints is large, a large number of control places should be added to the system. In this paper, a method is presented to assign the small number of constraints to forbidden states using some states which cover the forbidden states. So, a small number of control places are added to the system leading to obtaining a maximally permissive controller.     

Knowledge discovery in corporate events by neural network rule extraction

Over the last two decades, artificial neural networks (ANN) have been applied to solve a variety of problems such as pattern classification and function approximation. In many applications, it is desirable to extract knowledge from trained neural networks for the users to gain a better understanding of the network’s solution. In this paper, we use a neural network rule extraction method to extract knowledge from 2222 dividend initiation and resumption events. We find that the positive relation between the short-term price reaction and the ratio of annualized dividend amount to stock price is primarily limited to 96 small firms with high dividend ratios. The results suggest that the degree of short-term stock price underreaction to dividend events may not be as dramatic as previously believed. The results also show that the relations between the stock price response and firm size is different across different types of firms. Thus, drawing the conclusions from the whole dividend event data may leave some important information unexamined. This study shows that neural network rule extraction method can reveal more knowledge from the data.     

Incremental application of knowledge to continuously arriving time-oriented data

In our previous work, we introduced a computational architecture that effectively supports the tasks of continuous monitoring and of aggregation querying of complex domain meaningful time-oriented concepts and patterns (temporal abstractions), in environments featuring large volumes of continuously arriving and accumulating time-oriented raw data. Examples include provision of decision support in clinical medicine, making financial decisions, detecting anomalies and potential threats in communication networks, integrating intelligence information from multiple sources, etc. In this paper, we describe the general, domain-independent but task-specific problem-solving method underling our computational architecture, which we refer to as incremental knowledge-based temporal abstraction (IKBTA). The IKBTA method incrementally computes temporal abstractions by maintaining persistence and validity of continuously computed temporal abstractions from arriving time-stamped data. We focus on the computational framework underlying our reasoning method, provide well-defined semantic and knowledge requirements for incremental inference, which utilizes a logical model of time, data, and high-level abstract concepts, and provide a detailed analysis of the computational complexity of our approach.