水蒸气引起感烟火灾探测器误报研究

针对水蒸气这种典型的火灾探测误报源．在自行研制的火灾探测综合模拟实验平台中,通过收集水沸腾形成的水雾模拟水蒸气干扰源,研究了冬季与夏季,即不同环境温度务件下,水蒸气对散射型光电感烟火灾探测器的误报影响。通过比较感烟火灾探测器的输出值与其附近湿度变化曲线,分析了误报发生的原因。结果表明在气温较低的冬季,水蒸气较易形成液态小水珠,容易导致感烟火灾探测器误报;而在夏季由于气温较高,这种误报不易发生。     

多传感器耦合区域火灾报警技术研究

早期预警与低误报率一直是建筑火灾探测面临的挑战与难题。已有研究多针对特殊场所特定燃烧产物或多种传感器耦合,普适性不强,探测设备成本高,无法大规模应用。通过将火灾烟气蔓延规律与探测器信号时空分布融合,在不增加探测器数量和分布的情况下,提出了一种基于建筑结构微元的多传感器耦合区域火灾报警模型。对典型火灾场景的烟气蔓延情况进行了模拟分析,狭长结构中探测器信号强度变化具有一定的规律性。应用区域火灾报警模型后,报警时间较传统模式提前了14.7%,基本杜绝了单个探测器误报引发建筑物火灾报警的问题。结果表明:多传感器耦合探测模式显著缩短了火灾报警时间,降低了火灾探测误报率,实现了火灾的早期准确识别与预警。     

潮湿引起感烟火灾探测器误报研究

针对潮湿现象成为典型的火灾探测误报源 ,在火灾探测综合模拟实验平台中 ,通过收集水沸腾形成的水雾进行加湿 ,研究了冬季与夏季潮湿环境 ,对散射型光电感烟火灾探测器的发生误报影响。通过比较探测器的输出值与其附近湿度变化曲线 ,从原理上分析了误报发生的原因。实验结果表明 :在气温较低的冬季 ,水蒸气较易形成液态小水珠 ,容易导致感烟探测器误报警 ,而在夏季由于气温较高 ,这种情况较难发生。对相对湿度较高或达到饱和湿度 ,以及非火灾因素的雾气或微细水珠 ,对感烟探测器可靠性的影响 ,开展下进一步的工作进行了展望     

Abnormal karyotype in the chorion,not confirmed in a subsequently aborted fetus

An abnormal fetal karyotype, containing a del 16(q21-qter) as an extra chromosome, was diagnosed in all 14 metaphases examined in a sample of chorionic villous biopsy material. After elective abortion a mosaicism for this cell-line together with a normal one was detected in the chorionic tissue. Fibroblast cultures from several fetal skin biopsies all revealed a normal karyotype.     

False feedings at the nests of carrion crows<Emphasis Type="Italic"> Corvus corone corone</Emphasis>

False feedings, when individuals visit the nest but refrain from feeding the chicks, occur in some cooperative species and have been interpreted in the white-winged chough (Corcorax melanorhamphos) as active deception by helpers towards the rest of the group. In a cooperatively breeding population of carrion crows (Corvus corone corone) 81.5% of the individuals that provided nestling care showed various kinds of false feedings: arriving at the nest with no food, consuming part or all the food brought to the nest, or taking back from a chicks gape the food that had just been delivered. False feedings occurred on average during 16.3% of nest visits, with some individuals performing them at very high rates (up to 64% of nest visits). False feedings occurred at similar rates in unassisted pairs and groups with helpers, and breeding females showed false feeding at significantly higher rates than other group members. Furthermore, individuals showed false feedings regardless of whether they were alone on the nest or in the presence of other group members, and false feedings did not provoke aggression by the rest of the group. False feedings are not likely to represent deceptive help in the carrion crow. We suggest that crows evaluate the chicks condition during nest visits and that false feedings occur as result of a trade-off between their own hunger and the chicks needs.Communicated by W.A. Searcy     

Fault diagnosis of pipeline and pump unit systems using status coupling analysis

Earlier studies on fault diagnosis of the pipeline and pump unit systems (PPU) relied mainly on independent equipment analyses, which usually lead to false alarms because of the loss of information fusion. The aim of this study is to utilize the status coupling relationship to improve fault detection sensitivity and reduce false alarm rate. A real-time status identification of related equipment step is added between capturing abnormal signals and listing out diagnosis results. For example, when the pipeline pressure fluctuation is found abnormal, a status analysis of pump units is performed immediately, if the pump units are proven to be operational normally, then the pipeline leak alarm is acknowledged valid. The logical reasoning algorithm is used to capture abnormal conditions of pipeline pressures. The pump unit faults are captured by combining information from multiple sources. Field applications show that the proposed method significantly improves the PPU fault detection capability on fault detection sensitive and accuracy.     

Using biological data from field studies with multiple reference sites as a basis for environmental management: the risks for false positives and false negatives

Field surveys of biological responses can provide valuable information about environmental status and anthropogenic stress. However, it is quite usual for biological variables to differ between sites or change between two periods of time also in the absence of an impact. This means that there is an obvious risk that natural variation will be interpreted as environmental impact, or that relevant effects will be missed due to insufficient statistical power. Furthermore, statistical methods tend to focus on the risks for Type-I error, i.e. false positives. For environmental management, the risk for false negatives is (at least) equally important. The aim of the present study was to investigate how the probabilities for false positives and negatives are affected by experimental set up (number of reference sites and samples per site), decision criteria (statistical method and α-level) and effect size. A model was constructed to simulate data from multiple reference sites, a negative control and a positive control. The negative control was taken from the same distribution as the reference sites and the positive control was just outside the normal range. Using the model, the probabilities to get false positives and false negatives were calculated when a conventional statistical test, based on a null hypothesis of no difference, was used along with alternative tests that were based on the normal range of natural variation. Here, it is tested if an investigated site is significantly inside (equivalence test) and significantly outside (interval test) the normal range. Furthermore, it was tested how the risks for false positives and false negatives are affected by changes in α-level and effect size. The results of the present study show that the strategy that best balances the risks between false positives and false negatives is to use the equivalence test. Besides tests with tabulated p-values, estimates generated using a bootstrap routine were included in the present study. The simulations showed that the probability for management errors was smaller for the bootstrap compared to the traditional test and the interval test.     

Model-based information fusion investigation on fault isolation of subsea systems considering the interaction among subsystems and sensors

The offshore oil industry has expanded to deep water and Arctic. The harsh operating conditions (e.g., ice and strong wind) and increasing complicated system raise the occurrence likelihood of system faults. This requires timely fault isolation and management in the subsea system. However, the offshore oil industry mainly relies on humans to isolate faults based on alarms. With harsh operating conditions and increasing complicated system, this industry urgently needs research on more efficient fault isolation and cause diagnosis methods. Unfortunately, limited research is conducted on fault isolation method in the offshore oil industry. Furthermore, in industry 4.0 era, large amounts of information are obtained. This provides precondition for the application of information fusion technique which aims to improve diagnosis results. However, to the authors’ knowledge, information fusion has not been much studied in the fault isolation of the offshore oil industry. Moreover, the interaction of different subsystems contains valuable information. How the interaction of different subsystems can influence the fault diagnosis has not been explored. This paper proposes a Bayesian network (BN) based method for timely fault isolation and cause diagnosis for the offshore oil industry. The work fuses different information, and it also includes the dependency among different subsystems in the fault diagnosis. As an important alarm source, false alarms are also taken into account in the model. A case study on the subject of the subsea wellhead and chemical injection systems is conducted to demonstrate the functions and merits of the proposed method.     

Combining FAP,MAP and correlation analysis for multivariate alarm thresholds optimization in industrial process

In the real industrial process, alarm threshold optimization is an important part of alarm system rationalization. If the design of alarm threshold is unreasonable, it would result in nuisance alarms, among which the critical alarms are overwhelmed. In order to alleviate this phenomenon, we propose a method of multivariate alarm thresholds optimization to reduce the nuisance alarms. Firstly, causal relationship between process variables is constructed based on the time delay estimation method, thus we can determine the alarms propagation path and then select the optimized variables. Secondly, in order to guarantee both the process safety and correlation consistency, three factors - false alarm probability (FAP), missed alarm probability (MAP), and the correlation between the alarm information and process information – are combined to establish the objective function of the optimization process for the first time. Then, the optimal thresholds are obtained by the genetic algorithm. Finally, the validity and effectiveness of the developed method are illustrated by the Tennessee Eastman process.     

Application of internal standard method in recombinant luminescent bacteria test

Mercury and its organic compounds have been of severe concern worldwide due to their damage to the ecosystem and human health. The development of effective and affordable technology to monitor and signal the presence of bioavailable mercury is an urgent need. The Mer gene is a mercury-responsive resistant gene, and a mercury-sensing recombinant luminescent bacterium using the Mer gene was constructed in this study. The mer operon from marine Pseudomonas putida strain SP1 was amplified and fused with prompterless luxCDABE in the pUCD615 plasmid within Escherichia coli cells, resulting in pTHE30–E. coli. The recombinant strain showed high sensitivity and specificity. The detection limit of Hg^2 + was 5 nmol/L, and distinct luminescence could be detected in 30 min. Cd^2 +, Cu^2 +, Zn^2 +, Ca^2 +, Pb^2 +, Mg^2 +, Mn^2 +, and Al^3 + did not interfere with the detection over a range of 10^− 5–1 mM. Application of recombinant luminescent bacteria testing in environmental samples has been a controversial issue: especially for metal-sensing recombinant strains, false negatives caused by high cytotoxicity are one of the most important issues when applying recombinant luminescent bacteria in biomonitoring of heavy metals. In this study, by establishing an internal standard approach, the false negative problem was overcome; furthermore, the method can also help to estimate the suspected mercury concentration, which ensures high detection sensitivity of bioavailable Hg^2 +.