气管上段腺样囊性癌的临床病理

目的　探讨气管上段腺样囊性癌的临床病理特征及诊断和鉴别诊断要点。方法　收集2000年1月至2014年2月在北京协和医院确诊的4例气管上段腺样囊性癌病例，通过光镜、免疫组织化学及组织化学染色分析其临床病理特征、免疫组织化学特点、诊断及鉴别诊断要点。结果　4例气管上段腺样囊性癌患者中，男1例，女3例，平均年龄47岁(38~57岁）；既往均无腺样囊性癌病史，1例患者既往有结节性甲状腺肿手术史。镜下检查4例均为筛状/管状型腺样囊性癌，3例累及甲状腺组织，3例累及神经组织，未见淋巴结受累。免疫组织化学染色示4例P16、CD117、BCL2、P63、SMA均阳性，Ki67指数平均8%，TTF1和P53均阴性；4例中基底膜样物胶原Ⅳ阳性；组织化学染色示AB/PAS阳性。术后均接受总剂量为48~56 Gy的放疗，随访6~120个月，平均72.5个月，1例术后96个月复发，3例随访期间无复发及转移。结论　气管上段腺样囊性癌是罕见的原发于气管的低度恶性肿瘤，肿瘤生长缓慢，就诊时多数已侵及甲状腺组织，需要与原发于甲状腺的恶性肿瘤相鉴别，特别是在甲状腺穿刺及术中冰冻检查时。结合电子喉气管镜下表现、典型的形态学及免疫组织化学和组织化学染色有助于准确诊断。手术难以切除干净，术后放疗对延缓疾病复发有一定帮助。     

A Study on Turbo—rotor Multi—fault Diagnosis Based on a Neural Network

The multi-fault phenomena are common in the turbo-rotor system of a liquid rocket engine.As it has many excellent qualities,the neural network might be used to solve the problems of multi-fault diagnasis of a turbo-rotor system.First,the feature expression of a common turbo-rotor fault was studied in order to build up the standard fault pattern and satisfy the need of neural network studying and diagnosing.Then.the turbo-rotor fault identification and diagnosis problems were investigated by using a BP(back-propaga-tion)neural network.According to the BP neural network problems,the parallel BP neural network method of multi-fault diagnosis and classification was presented and investigated.The results indicated that the parallel Bp neural network method could solve the turbo-rotor multi-fault diagnosis problems.     

平面近场测试系统在天线测试和诊断中的应用

介绍了天线近场测量的基本原理和HD-1型平面近场测试系统,并对近场测量在实际天线测试中的应用情况、发展方向和应用前景等作了简单的描述。     

Assessing continuous bivariate effects among different groups through nonparametric regression models: An application to breast cancer detection

In many applications, the joint effect of two continuous covariates on the target binary response may vary across groups defined by levels of a given factor. A testing procedure that would enable this type of surface-by-factor interactions to be detected has been designed. To accomplish this goal, a logistic generalized additive model (GAM) with bivariate continuous interactions varying across groups defined by levels of a factor is considered. A local scoring algorithm based on local linear kernel smoothers was implemented to estimate the proposed logistic GAM. Bootstrap resampling techniques were used for the purpose of testing for factor-by-surface interactions. Given the high computational cost involved, binning techniques were used to speed up computation in the estimation and testing processes. The adequacy of the bootstrap-based test was assessed by means of a simulation study. If a factor-by-surface interaction is detected in the model, it is then established that the use of the odds-ratio curves is very useful in obtaining a direct interpretation of the fitted model. The benefits of using this methodology when analyzing real data are illustrated by applying the technique to the outputs produced by a computerized system dedicated to the early detection of breast cancer.     

一种自制硬件诊断磁带的方法

牛哲成．一种自制硬件诊断磁带的方法．测并技术，１９９４，１８（２）：１４９～１５４硬件诊断磁带是由用于对计算机硬件系统各组成部分进行功能检查、状态测试和故障检测的诊断测试程序集构成的一种专用工具磁带。本文提出了一种用户在ＰＥ３２００系列计算机裸机环境下自己制作硬件诊断磁带的方法，井涉及到具体的程序实现。用该方法制作的硬件诊断磁带操作简单。对进一步搞好ＰＥ３２００系列计算机的维护，开发完善ＰＥ３２００系列计算机硬件诊断系统具有实际意义。     

在线磨辊辊面粗糙度的实验研究

影响轧辊表面粗糙度的因素很多，为此对影响辊面粗糙度的因素进行了系统分析和实验研究，得出了轧辊和砂轮的速度、砂轮粒度、砂轮直径及磨削液等主要因素对轧辊表面粗糙度影响的一般规律。利用回归分析的方法建立了轧辊表面粗糙度的数学模型，并将计算值与实测值进行了比较，发现两者基本一致。此项研究为适时控制冷轧带钢在线磨辊辊面粗糙度提供了计算模型。     

生产装置在线模拟与优化技术的应用

概述了以通用软件为基础开发化工生产装置严格在线模拟与优化系统的过程，介绍了在线模型的５个层次及其特点，以合成氨为例说明了在线模型的运行和应用情况。     

模糊数学在电视机故障诊断中的应用

本文介绍了应用模糊数学方法进行计算机辅助诊断电视机的故障原因,这种方法有效性在于建立模型的因果关系正确性和自动搜索范围及速度。     

人工智能方法用于熔喷过程在线检测的研究

熔喷生产最终产品质量的控制，对于过程优化具有重要的意义。产品质量的在线检测是过程监测与质量控制必不可少的。本文首次在熔喷系统中引入人工智能的研究方法，对熔喷非织造布的厚度与纤维直径进行在线预测。实测数据与人工神经网络的预测结果吻合较好，从而证实了人工神经网络用于模拟熔喷过程的可行性。网络的输入包括：挤出机温度、喷头温度、熔融体挤出速率、空气流率及接收距离。神经元计算的输出是纤维直径与熔喷非织布的厚度。文中进一步提出了将神经网络与专家系统相结合的研究路线，以实现熔喷过程的优化控制。     

A fuzzy diagnosis approach using dynamic fault trees

By incorporating digraph models, fault trees and fuzzy inference mechanisms in a unified framework, a novel approach for fault diagnosis is developed in this work. To relieve the on-line computation load, the fault origins considered in diagnosis are limited to the basic events in the cut sets of a given fault tree. The symptom occurrence order associated with each root cause is derived from system digraph with the qualitative simulation techniques. The implied candidate patterns are enumerated according to two proposed theorems and then encoded in the inference system with IF-THEN rules. The simulation results show that the proposed approach is not only feasible but also capable of identifying the most likely cause(s) of a hazardous event at the earliest possible time.