STRESS GRADIENTS AROUND NOTCHES

Abstract— Stress gradients at the root of a notch are significant for the notch effect and the size effect of fatigue properties. Usually the gradient of the stress distribution in the minimum section is considered. In the present paper the variation of the tensile stress along the edge of the notch is considered. Calculations are made for a variety of notches. The results indicate a remarkable conformity of stress distributions at the notch root if the same peak stress and notch root radius ( ρ ) apply. Consequently K ₁and ρare highly characteristic for the stress distribution around the notch. Along the edge of the notch the stress decreases at a much slower rate than in the minimum section going away from the material surface. For the stress along the edge of the notch a stress gradient coefficient is defined. The variation of this coefficient is fairly small for several notches and K ₁, values. A 5% lower stress as compared to the peak stress at the notch root is obtained at about 0.02 ρbelow the material surface and at a distance of about 0.18 ρalong the material surface.     

The law as a dynamic interconnected system of states of affairs: a legal top ontology

In this paper, an abstract model of the law is presented that has three primitives: states of affairs, events and rules. The starting point of the abstract model is that the law is a dynamic system of states of affairs which are connected by means of rules and events. The abstract model can be regarded as a top ontology of the law, that can be applied to legal knowledge representation. After an elaboration of the three primitives, the uses of the abstract model are illustrated by the analysis of central topics of law. Then we discuss heuristic guidelines for legal knowledge representation that are suggested by the abstract model. The paper concludes with a comparison with related work. The appendix contains a formalism for the abstract model.     

A principled approach to developing legal knowledge systems

In this article we present a principled, four-phased approach to the development of legal knowledge systems. We set out from the well-studied CommonKADS method for the development of knowledge systems and tailor this method to the legal domain. In particular, we propose a generic legal ontology, and describe the creation of statute-specific ontologies to adopt the method for building legal systems. In the construction of these ontologies, we start from a theoretical analysis of the legal domain. The well-known example of the Imperial College Library Regulations (ICLR) is used to illustrate the method.     

变频调速灯泡贯流泵装置结构开发与优化

针对南水北调工程江苏段和山东段低扬程泵站的具体特点,开发了新型结构的灯泡贯流泵机组,并采用变频调速装置进行工况调节.根据使用常规电动机和永磁电动机两种设计方案,进行了灯泡体尺寸和出水流道型线优化设计,利用商用软件CFX对贯流泵装置内部流场开展内部流动数值分析和性能预测.数值计算和模型装置试验结果表明,合理增加流道外壳直径和减小电动机外径,都能有效提高贯流泵装置效率,不同特征扬程下效率提高0.5～3.7%.     

Recovery of Unrehearsed Items in Connectionist Models

When gradient-descent models with hidden units are retrained on a portion of a previously learned set of items, performance on both the relearned and unrelearned items improves. Previous explanations of this phenomenon have not adequately distinguished recovery, which is dependent on original learning, from generalization, which is independent of original learning. Using a measure of vector similarity to track global changes in the weight state of three-layer networks, we show that (a) unlike in networks without hidden units, recovery occurs in the absence of generalization in networks with hidden units, and (b) when the conditions of learning are varied, changes in the extent of recovery are reflected in changes in the extent to which the weights move back towards their values held after original learning. The implications of this work for rehabilitation studies, human relearning and models of human long-term memory are also considered.     

Hypertransfer in Neural Networks

The Osgood surface for transfer in human associative learning is introduced (Osgood, 1949). It describes the relationship between stimulus and response similarities and transfer of learning. In this paradigm, first a list A is learned, then a list B, followed by retesting on list A. Simulation results indicate that three-layer networks with backpropagation do not only show 'catastrophic interference' but also 'hypertransfer'. Two-layer networks do not suffer from this. Hypertransfer is explained with reference to hidden-layer representations formed during learning. Since it cannot account for this very general trait of human behavior, backpropagation's role as a tool for models of human memory must be watched very carefully.