A knowledge-based system for sensor selection

This paper presents a Knowledge-based system (KBS) developed to allow users, who may not be knowledgeable about sensors, to select sensors suitable for their specific needs. The KBS runs on a micro-computer. The selection criteria are user specified and are based on the desired measurement parameters. The system output includes all of the operational and dimensional parameters of the recommended sensor, price, and vendor information.     

标准椅全身计数器对不同年龄组均匀体模源全能峰效率的刻度

本文介绍了用标准液体源均匀充入聚氯乙烯制的体模,刻度标准椅全身计数器〔φ8″×4″(NaI)〕的方法和结果。分别就代表6个年龄组的体模,测定了标准椅全身计数器对5种能量(364,637,898,1460和1836keV)γ射线的全能峰效率;给出了全能峰效率与γ射线能量和被测对象体重的关系曲线。     

北京谱仪端盖簇射计数器工作气体的研究

为使北京谱仪端盖族射计数器更好地工作在自猝灭流光ＳＱＳ模式下，提高其能量分辨以及线性等指标，用Ａｒ，ＣＯ２，Ｌｓｏｂｕｔａｎｅ三元工作气体对探测器的工作性能进行了仔细研究，找出了一适合于主雇地数器的工作气体，此外，还研究了异丁的纯度对探测器性能的影响。     

Integrated scheduling of flexible manufacturing systems employingautomated guided vehicles

Centralized and distributed automated guided vehicle system (AGVS) models for materials handling, and the model for part processing are integrated into a single coherent model. This formulation can be used to collectively schedule and control the entire flexible manufacturing system (FMS) as opposed to the traditional separate scheduling of part processing and material handling. The two AGVS models are based on Petri nets and can be directly used in the scheduling method that uses Petri nets for formulation and heuristic search for solution. This method employs a global search to seek the optimal operation of an entire FMS. Scheduling examples are presented and the method compares favorably with the results simulated using heuristic dispatch rules     

Do We Stand on Common Ground? A Threat Appraisal Model for Terror Alerts Issued by the Department of Homeland Security

The fabric and faces of threat, and the expediency and efficiency in the communication of threat, are examined with a threat appraisal model. This model is empirically tested on an ongoing communication challenge, the issuance of terror alerts by the United States' Department of Homeland Security (DHS), focusing on how threat is appraised by both the conservative and liberal audiences. Findings showed a shared view by the DHS and conservative audiences on the levels and nature of threats; liberal audiences thought otherwise. Though there appeared to be a consensus between the conservative and liberal audiences on the efficacy of threat communication, more internal consistency within DHS is needed to optimise its effectiveness.     

Purification and renaturation of recombinant human lymphotoxin (tumour necrosis factor beta) expressed in Escherichia coli as inclusion bodies

High level expression of recombinant human tumour necrosis factor β (rh TNF-β) in Escherichia coli results in the formation of two portions of protein, namely soluble active protein and insoluble protein which is inactive and aggregates in the form of inclusion bodies (IBs). In this study, a procedure for purification and renaturation of rh TNF-β from inclusion bodies has been designed and verified experimentally with a product purity of more than 90% and a recovery of about 30%. The procedure includes washing of IBs with specific wash buffer (Triton X-100/EDTA/lysozyme/PMSF), their solubilization with 8 mol dm^?3 alkaline urea, purification with ion-exchange columns, refolding with renaturation buffer and finally concentration and desalination with an ultrafiltration membrane. The characteristics of the renatured protein were identical with those of purified protein from the soluble fraction as demonstrated by (1) SDS-PAGE, (2) cytotoxic activity on mouse L929 cells, (3) N-terminal amino acid sequence, and (4) gel filtration chromatography.     

Application of artificial neural networks to the analysis of two‐dimensional fluorescence spectra in recombinant E coli fermentation processes

A two‐dimensional (2D) spectrofluorometer was used to monitor various fermentation processes with recombinant E coli for the production of 5‐aminolevulinic acid (ALA). The whole fluorescence spectral data obtained during a process were analyzed using artificial neural networks, ie self‐organizing map (SOM) and feedforward backpropagation neural network (BPNN). The SOM‐based classification of the whole spectral data has made it possible to qualitatively associate some process parameters with the normalized weights and variances, and to select some useful combinations of excitation and emission wavelengths. Based on the classified fluorescence spectra a supervised BPNN algorithm was used to predict some of the process parameters. It was also shown that the BPNN models could elucidate some sections of the process's performance, eg forecasting the process's performance. Copyright © 2005 Society of Chemical Industry