Monitoring charged particle and ion emissions from a laser printer

While the emission rate of ultrafine particles has been measured and quantified, there is very little information on the emission rates of ions and charged particles from laser printers. This paper describes a methodology that can be adopted for measuring the surface charge density on printed paper and the ion and charged particle emissions during operation of a high-emitting laser printer and shows how emission rates of ultrafine particles, ions and charged particles may be quantified using a controlled experiment within a closed chamber.

A human-simulated immune evolutionary computation approach

Premature convergence to local optimal solutions is one of the main difficulties when using evolutionary algorithms in real-world optimization problems. To prevent premature convergence and degeneration phenomenon, this paper proposes a new optimization computation approach, human-simulated immune evolutionary algorithm (HSIEA). Considering that the premature convergence problem is due to the lack of diversity in the population, the HSIEA employs the clonal selection principle of artificial immune system theory to preserve the diversity of solutions for the search process. Mathematical descriptions and procedures of the HSIEA are given, and four new evolutionary operators are formulated which are clone, variation, recombination, and selection. Two benchmark optimization functions are investigated to demonstrate the effectiveness of the proposed HSIEA.

Monitoring charged particles in indoor air using a neutral cluster and air ion spectrometer

While there are sources of ions both outdoors and indoors, ventilation systems can introduce as well as remove ions from the air. As a result, indoor ion concentrations are not directly related to air exchange rates in buildings. In this study, we attempt to relate these quantities with the view of understanding how charged particles may be introduced into indoor spaces.

A practical signal processing approach for condition monitoring of low speed machinery using Peak-Hold-Down-Sample algorithm

A simple and effective down-sample algorithm, Peak-Hold-Down-Sample (PHDS) algorithm is developed in this paper to enable a rapid and efficient data transfer in remote condition monitoring applications. The algorithm is particularly useful for high frequency Condition Monitoring (CM) techniques, and for low speed machine applications since the combination of the high sampling frequency and low rotating speed will generally lead to large unwieldy data size. The effectiveness of the algorithm was evaluated and tested on four sets of data in the study. One set of the data was extracted from the condition monitoring signal of a practical industry application. Another set of data was acquired from a low speed machine test rig in the laboratory. The other two sets of data were computer simulated bearing defect signals having either a single or multiple bearing defects. The results disclose that the PHDS algorithm can substantially reduce the size of data while preserving the critical bearing defect information for all the data sets used in this work even when a large down-sample ratio was used (i.e., 500 times down-sampled). In contrast, the down-sample process using existing normal down-sample technique in signal processing eliminates the useful and critical information such as bearing defect frequencies in a signal when the same down-sample ratio was employed. Noise and artificial frequency components were also induced by the normal down-sample technique, thus limits its usefulness for machine condition monitoring applications.

Human immunodeficiency virus type 1 subtype C Gag virus-like particle boost substantially improves the immune response to a subtype C gag DNA vaccine in mice

Human immunodeficiency virus type 1 (HIV-1) subtype C is the predominant HIV in southern Africa, and is the target of a number of recent vaccine candidates. It has been proposed that a heterologous prime/boost vaccination strategy may result in stronger, broader and more prolonged immune responses. Since HIV-1 Gag Pr55 polyprotein can assemble into virus-like particles (VLPs) which have been shown to induce a strong cellular immune response in animals, we showed that a typical southern African subtype C Pr55 protein expressed in insect cells via recombinant baculovirus could form VLPs. We then used the baculovirus-produced VLPs as a boost to a subtype C HIV-1 gag DNA prime vaccination in mice. This study shows that a low dose of HIV-1 subtype C Gag VLPs can significantly boost the immune response to a single subtype C gag DNA inoculation in mice. These results suggest a possible vaccination regimen for humans. © 2004 SGM.

HIV-1 sub-type C chimaeric VLPs boost cellular immune responses in mice

Several approaches have been explored to eradicate HIV; however, a multigene vaccine appears to be the best option, given their proven potential to elicit broad, effective responses in animal models. The Pr55 Gagprotein is an excellent vaccine candidate in its own right, given that it can assemble into large, enveloped, virus-like particles (VLPs) which are highly immunogenic, and can moreover be used as a scaffold for the presentation of other large non-structural HIV antigens. In this study, we evaluated the potential of two novel chimaeric HIV-1 Pr55 Gag-based VLP constructs - C-terminal fusions with reverse transcriptase and a Tat::Nef fusion protein, designated GagRT and GagTN respectively - to enhance a cellular response in mice when used as boost components in two types of heterologous prime-boost vaccine strategies. A vaccine regimen consisting of a DNA prime and chimaeric HIV-1 VLP boosts in mice induced strong, broad cellular immune responses at an optimum dose of 100 ng VLPs. The enhanced cellular responses induced by the DNA prime-VLP boost were two- to three-fold greater than two DNA vaccinations. Moreover, a mixture of GagRT and GagTN VLPs also boosted antigen-specific CD8+ and CD4+ T-cell responses, while VLP vaccinations only induced predominantly robust Gag CD4+ T-cell responses. The results demonstrate the promising potential of these chimaeric VLPs as vaccine candidates against HIV-1. © 2010 Pillay et al; licensee BioMed Central Ltd.

Utilising Reliability and Condition Monitoring Data for Asset Health Prognosis

The ability to forecast machinery health is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models which attempt to forecast machinery health based on condition data such as vibration measurements. This paper demonstrates how the population characteristics and condition monitoring data (both complete and suspended) of historical items can be integrated for training an intelligent agent to predict asset health multiple steps ahead. The model consists of a feed-forward neural network whose training targets are asset survival probabilities estimated using a variation of the Kaplan–Meier estimator and a degradation-based failure probability density function estimator. The trained network is capable of estimating the future survival probabilities when a series of asset condition readings are inputted. The output survival probabilities collectively form an estimated survival curve. Pump data from a pulp and paper mill were used for model validation and comparison. The results indicate that the proposed model can predict more accurately as well as further ahead than similar models which neglect population characteristics and suspended data. This work presents a compelling concept for longer-range fault prognosis utilising available information more fully and accurately.

Peripheral compartment innate immune response to Haemophilus influenzae and Streptococcus pneumoniae in chronic obstructive pulmonary disease patients.

Alterations in innate immunity that predispose to chronic obstructive pulmonary disease (COPD) exacerbations are poorly understood. We examined innate immunity gene expression in peripheral blood polymorphonuclear leukocytes (PMN) and monocytes stimulated by Haemophilus influenzae and Streptococcus pneumoniae. Thirty COPD patients (15 rapid and 15 non-rapid lung function decliners) and 15 smokers without COPD were studied. Protein expression of IL-8, IL-6, TNF-α and IFN-γ (especially monocytes) increased with bacterial challenge. In monocytes stimulated with S. pneumoniae, TNF-α protein expression was higher in COPD (non-rapid decliners) than in smokers. In co-cultures of monocytes and PMN, mRNA expression of TGF-β1 and MYD88 was up-regulated, and CD14, TLR2 and IFN-γ down-regulated with H. influenzae challenge. TNF-α mRNA expression was increased with H. influenzae challenge in COPD. Cytokine responses were similar between rapid and non-rapid decliners. TNF-α expression was up-regulated in non-rapid decliners in response to H. influenzae (monocytes) and S. pneumoniae (co-culture of monocytes and PMN). Exposure to bacterial pathogens causes characteristic innate immune responses in peripheral blood monocytes and PMN in COPD. Bacterial exposure significantly alters the expression of TNF-α in COPD patients, although not consistently. There did not appear to be major differences in innate immune responses between rapid and non-rapid decliners.

Underground power cable environment on-line monitoring and analysis

Knowledge of cable parameters has been well established but a better knowledge of the environment in which the cables are buried lags behind. Research in Queensland University of Technology has been aimed at obtaining and analysing actual daily field values of thermal resistivity and diffusivity of the soil around power cables. On-line monitoring systems have been developed and installed with a data logger system and buried spheres that use an improved technique to measure thermal resistivity and diffusivity over a short period. Results based on long term continuous field data are given. A probabilistic approach is developed to establish the correlation between the measured field thermal resistivity values and rainfall data from weather bureau records. This data from field studies can reduce the risk in cable rating decisions and provide a basis for reliable prediction of “hot spot” of an existing cable circuit

Monitoring smartphones for anomaly detection

In this paper we demonstrate how to monitor a smartphone running Symbian operating system and Windows Mobile in order to extract features for anomaly detection. These features are sent to a remote server because running a complex intrusion detection system on this kind of mobile device still is not feasible due to capability and hardware limitations. We give examples on how to compute relevant features and introduce the top ten applications used by mobile phone users based on a study in 2005. The usage of these applications is recorded by a monitoring client and visualized. Additionally, monitoring results of public and self-written malwares are shown. For improving monitoring client performance, Principal Component Analysis was applied which lead to a decrease of about 80 of the amount of monitored features.

Real-time risk monitoring in business processes : a sensor-based approach

This article proposes an approach for real-time monitoring of risks in executable business process models. The approach considers risks in all phases of the business process management lifecycle, from process design, where risks are defined on top of process models, through to process diagnosis, where risks are detected during process execution. The approach has been realized via a distributed, sensor-based architecture. At design-time, sensors are defined to specify risk conditions which when fulfilled, are a likely indicator of negative process states (faults) to eventuate. Both historical and current process execution data can be used to compose such conditions. At run-time, each sensor independently notifies a sensor manager when a risk is detected. In turn, the sensor manager interacts with the monitoring component of a business process management system to prompt the results to process administrators who may take remedial actions. The proposed architecture has been implemented on top of the YAWL system, and evaluated through performance measurements and usability tests with students. The results show that risk conditions can be computed efficiently and that the approach is perceived as useful by the participants in the tests.