Optimal transfer trees and distinguishing trees for testing observable nondeterministic finite-state machines

The fault-state detection approach for blackbox testing consists of two phases. The first is to bring the system under test (SUT) from its initial state to a targeted state t and the second is to check various specified properties of the SUT at t. This paper investigates the first phase for testing systems specified as observable nondeterministic finite-state machines with probabilistic and weighted transitions. This phase involves two steps. The first step transfers the SUT to some state t' and the second step identifies whether t' is indeed the targeted state t or not. State transfer is achieved by moving the SUT along one of the paths of a transfer tree (TT) and state identification is realized by using diagnosis trees (DT). A theoretical foundation for the existence and characterization of TT and DT with minimum weighted height or minimum average weight is presented. Algorithms for their computation are proposed.     

A distributed adverse drug reaction detection system using intelligent agents with a fuzzy recognition‐primed decision model

Discovering unknown adverse drug reactions (ADRs) in postmarketing surveillance as early as possible is highly desirable. Nevertheless, current postmarketing surveillance methods largely rely on spontaneous reports that suffer from serious underreporting, latency, and inconsistent reporting. Thus these methods are not ideal for rapidly identifying rare ADRs. The multiagent systems paradigm is an emerging and effective approach to tackling distributed problems, especially when data sources and knowledge are geographically located in different places and coordination and collaboration are necessary for decision making. In this article, we propose an active, multiagent framework for early detection of ADRs by utilizing electronic patient data distributed across many different sources and locations. In this framework, intelligent agents assist a team of experts based on the well‐known human decision‐making model called Recognition‐Primed Decision (RPD). We generalize the RPD model to a fuzzy RPD model and utilize fuzzy logic technology to not only represent, interpret, and compute imprecise and subjective cues that are commonly encountered in the ADR problem but also to retrieve prior experiences by evaluating the extent of matching between the current situation and a past experience. We describe our preliminary multiagent system design and illustrate its potential benefits for assisting expert teams in early detection of previously unknown ADRs. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 827–845, 2007.     

Domain-Driven, Actionable Knowledge Discovery

Data mining increasingly faces complex challenges in the real-life world of business problems and needs. The gap between business expectations and R&D results in this area involves key aspects of the field, such as methodologies, targeted problems, pattern interestingness, and infrastructure support. Both researchers and practitioners are realizing the importance of domain knowledge to close this gap and develop actionable knowledge for real user needs.     

Neural networks and blood cell identification

The objective of this project is to propose a method of identifying cells found in human blood and to classify them based upon their morphological features using neural networks. The project focuses on three major blood cell types, namely, erythrocytes, leukocytes and platelets. The data are collected using peripheral blood smears from clinical patients. The image acquisition requires 100x magnification on all the blood smears, the preprocessing involves the use of median and edge enhance filters; the feature extraction is done by performing the wavelet transform on the images. Finally classification of the blood cell types is done using ALOPEX and Back Propagation trained neural networks. The efficacy of both networks is then compared by comparing their outputs and number of iterations required to reach the final result.     

Oxygen absorption kinetics in YBa2Cu3Ox

The oxygen absorption process in highly aligned YBa₂Cu₃O_x samples in air and in pure oxygen was studied at several constant temperatures. The process can be expressed in two steps, a chemical reaction controlled step and a diffusion controlled step. The kinetic equations are as follows respectively:

(1 − a_t) ln(1 − a_t) + a_t = k_Dt

The activation energy was calculated using an Arrhenius equation. The technical conditions of heat treatment for oxygen absorption of highly aligned YBa₂Cu₃O_x crystals are discussed.     

我国聚甲醛工业的供需分析

从国内聚甲醛树脂的生产、贸易及消费状况出发 ,分析了国内聚甲醛工业的供需现状并提出短期预测。     

新取代苯基荧光酮试剂的合成及其性质的研究Ⅱ．新型取代苯基荧光酮与金属离子显色反应的研究

研究了３，５－二溴－４－氨基苯基荧光酮、３，５－二溴－４－偶氮变色酸等８种苯基荧光酮在酸性介质中与高价金属离子Ｚｒ（Ⅳ）、Ｍｏ（Ⅵ）、Ｗ（Ⅵ）、Ａｌ（Ⅲ）、Ｇａ（Ⅲ）等的显色反应。３，５－位二溴取代的试剂的灵敏度高于未取代的灵敏度，摩尔吸光系数绝大多数在１０５Ｌ·ｍｏｌ－１·ｃｍ－１以上。     

Performance analysis of the bidirectional associative memory and animproved model from the matched-filtering viewpoint

This paper discusses the bidirectional associative memory (BAM) model from the matched-filtering viewpoint and offers it a new interpretation. Our attention is focused on the problem of stability and attractivity of equilibrium states. Several sufficient and/or necessary conditions are presented. To improve the BAM performance, an exponential function is used to enhance the correlations between the binary vectors of the retrieval key and that of the stored pattern similar to the key. The modified model is shown to be asymptotically stable. Theoretical analysis and simulation results demonstrate that the modified model performs much better than the original BAM in terms of memory capacity and error correction capability.     

用小尺寸R－CT试样研究核压力容器钢断裂韧性及与Charpy试样的比较

提出了一种用双边带深侧槽的小尺寸圆形紧凑拉伸试样评定核压力容器（ＲＰＶ）钢断裂韧性的单试作试验方法，给出了用该方法测定的两个厂家生产的核压力容器用Ａ５０８ＣＬ３钢的断裂韧性参数，还与Ｃｈａｒｐｙ试样的试验结果及大尺寸标准试样的试验结果进行了比较。研究结果表明：用双边带深侧槽的小尺寸Ｒ－ＣＴ试样测得的断裂韧性值比相同恻槽深度预制疲劳裂纹Ｃｈａｒｐｙ试样的测试值更接近有效断裂韧性值，所以，用于核压力容器断裂韧性的监测是可行的。