State detection of bond wires in IGBT modules using eddy current pulsed thermography

Insulated gate bipolar transistor (IGBT) modules are important safety critical components in electrical power systems. Bond wire lift-off, a plastic deformation between wire bond and adjacent layers of a device caused by repeated power/thermal cycles, is the most common failure mechanism in IGBT modules. For the early detection and characterization of such failures, it is important to constantly detect or monitor the health state of IGBT modules, and the state of bond wires in particular. This paper introduces eddy current pulsed thermography (ECPT), a nondestructive evaluation technique, for the state detection and characterization of bond wire lift-off in IGBT modules. After the introduction of the experimental ECPT system, numerical simulation work is reported. The presented simulations are based on the 3-D electromagnetic-thermal coupling finite-element method and analyze transient temperature distribution within the bond wires. This paper illustrates the thermal patterns of bond wires using inductive heating with different wire statuses (lifted-off or well bonded) under two excitation conditions: nonuniform and uniform magnetic field excitations. Experimental results show that uniform excitation of healthy bonding wires, using a Helmholtz coil, provides the same eddy currents on each, while different eddy currents are seen on faulty wires. Both experimental and numerical results show that ECPT can be used for the detection and characterization of bond wires in power semiconductors through the analysis of the transient heating patterns of the wires. The main impact of this paper is that it is the first time electromagnetic induction thermography, so-called ECPT, has been employed on power/electronic devices. Because of its capability of contactless inspection of multiple wires in a single pass, and as such it opens a wide field of investigation in power/electronic devices for failure detection, performance characterization, and health monitoring. 

Building pH Sensors into Paper-based Small-molecular Logic Sys-tems for Very Simple Detection of Edges of Objects

Genetically-engineered bacteria and reactive DNA networks detect edges of objects, as done in our retinas and as also found within computer vision. We now demonstrate that simple molecular logic systems (a combination of a pH sensor, a photo acid generator and a pH buffer spread on paper) without any organization can achieve this relatively complex computational goal with good-fidelity. This causes a jump in the complexity achievable by molecular logic-based computation and extends its applicability. The molecular species involved in light dose-driven 'off-on-off' fluorescence is diverted in the ‘on’ state by proton diffusion from irradiated to unirradiated regions where it escapes a strong quencher, thus visualizing the edge of a mask.

Development of Novel Activity-Based Probes for the Detection of Serine Proteases

Cystic Fibrosis (CF) is a genetic disease featuring a chronic cycle of inflammation and infection in the airways of sufferers. Mutations lead to altered ion transport, which in turn causes dehydrated airways and reduced mucociliary clearance which predisposes the patient to infection, resulting in a severe immune response and tissue destruction (1). Airway dehydration is primarily caused by the hyperabsorption of sodium by the epithelial sodium channel (ENaC) (2). ENaC is activated by the action of a number of predominantly trypsin-like Channel Activating Proteases (CAPs) including prostasin, matriptase and furin (3). Additional proteases known to activate ENaC include human airway trypsin (3), plasmin, neutrophil elastase and chymotrypsin (4).

Activity profiling is a valuable technique which involves the use of small inhibitory molecules called Activity-Based Probes (ABPs) which can be used to covalently label the active site of proteases and provide a range of information regarding its structure, catalytic mechanism, location and function within biological systems. The development of novel ABPs for CAPs, would enhance understanding of the role of these proteases in CF airways disease and in particular their role in ENaC activation and airway dehydration. This project investigates the application of a range of novel broad-spectrum ABPs targeting the various subclasses of serine proteases, to include those proteases involved in ENaC activation. Additionally, the application of more selective ABPs in detecting specific serine proteases is investigated.

Compounds were synthesised by Solid-Phase Peptide Synthesis (SPPS) using a standard Fmoc/tBu strategy. Kinetic evaluation of synthesised ABPs against various serine proteases was determined by fluorogenic steady-state enzyme assays. Furthermore, application of ABPs and confirmation of irreversible nature of the compounds was carried out through SDS-PAGE and electroblotting techniques.

Synthesised compounds showed potent irreversible inhibition of serine proteases within their respective targeting class (NAP855 vs Trypsin k3/Ki = 2.60 x 106 M-1 min-1, NFP849 vs Chymotrypsin k3/Ki = 1.28 x 106 M-1 min-1 and NVP800 vs Neutrophil Elastase k3/Ki = 6.41 x 104 M-1 min-1). Furthermore ABPs showed little to no cross-reactivity between classes and so display selectivity between classes. The irreversible nature of compounds was further demonstrated through labelling of proteases, followed by separation and detection via SDS-PAGE and electroblotting techniques. Targeted labelling of active proteases only, was demonstrated by failure of ABPs to detect previously inactivated proteases. Extension of the substrate recognition site within probes resulted in an increased potency and selectivity in the detection of the target proteases. Successful detection of neutrophil elastase from CF sputum samples by NVP800, demonstrated the application of compounds within biological samples and their potential use in identifying further proteases involved in ENaC activation and airway dehydration in CF patients.

A Game-Theoretic Approach for Threats Detection and Intervention in Surveillance

Threat prevention with limited security resources is a challenging problem. An optimal strategy is to eectively predict attackers' targets (or goals) based on current available information, and use such predictions to prevent (or disrupt) their planned attacks. In this paper, we propose a game-theoretic framework to address this challenge which encompasses the following three elements. First, we design a method to analyze an attacker's types in order to determine the most plausible type of an attacker. Second, we propose an approach to predict possible targets of an attack and the course of actions that the attackers may take even when the attackers' types are ambiguous. Third, a game-theoretic based strategy is developed to determine the best protection actions for defenders (security resources).

Detection Limits of Organic Compounds Achievable with Intense, Short-Pulse Lasers

Many organic molecules have strong absorption bands which can be accessed by ultraviolet short pulse lasers to produce efficient ionization. This resonant multiphoton ionization scheme has already been exploited as an ionization source in time-of-flight mass spectrometers used for environmental trace analysis. In the present work we quantify the ultimate potential of this technique by measuring absolute ion yields produced from the interaction of 267 nm femtosecond laser pulses with the organic molecules indole and toluene, and gases Xe, N2 and O2. Using multiphoton ionization cross sections extracted from these results, we show that the laser pulse parameters required for real-time detection of aromatic molecules at concentrations of one part per trillion in air and a limit of detection of a few attomoles are achievable with presently available commercial laser systems. The potential applications for the analysis of human breath, blood and tissue samples are discussed.

GALEX Detection of Shock Breakout in Type IIP Supernova PS1-13arp: Implications for the Progenitor Star Wind

We present the GALEX detection of a UV burst at the time of explosion of an optically normal supernova (SN) IIP (PS1-13arp) from the Pan-STARRS1 survey at z = 0.1665. The temperature and luminosity of the UV burst match the theoretical predictions for shock breakout in a red supergiant (RSG), but with a duration a factor of similar to 50 longer than expected. We compare the NUV light curve of PS1-13arp to previous GALEX detections of SNe IIP and find clear distinctions that indicate that the UV emission is powered by shock breakout, and not by the subsequent cooling envelope emission previously detected in these systems. We interpret the similar to 1 day duration of the UV signal with a shock breakout in the wind of an RSG with a pre-explosion mass-loss rate of similar to 10(-3) M-circle dot yr(-1). This mass-loss rate is enough to prolong the duration of the shock breakout signal, but not enough to produce an excess in the optical plateau light curve or narrow emission lines powered by circumstellar interaction. This detection of nonstandard, potentially episodic high mass loss in an RSG SN progenitor has favorable consequences for the prospects of future wide-field UV surveys to detect shock breakout directly in these systems, and provide a sensitive probe of the pre-explosion conditions of SN progenitors.

Loss-of-Main Monitoring and Detection for Distributed Generations Using Dynamic Principal Component Analysis

In this paper, our previous work on Principal Component Analysis (PCA) based fault detection method is extended to the dynamic monitoring and detection of loss-of-main in power systems using wide-area synchrophasor measurements. In the previous work, a static PCA model was built and verified to be capable of detecting and extracting system faulty events; however the false alarm rate is high. To address this problem, this paper uses a well-known ‘time lag shift’ method to include dynamic behavior of the PCA model based on the synchronized measurements from Phasor Measurement Units (PMU), which is named as the Dynamic Principal Component Analysis (DPCA). Compared with the static PCA approach as well as the traditional passive mechanisms of loss-of-main detection, the proposed DPCA procedure describes how the synchrophasors are linearly
auto- and cross-correlated, based on conducting the singular value decomposition on the augmented time lagged synchrophasor matrix. Similar to the static PCA method, two statistics, namely T2 and Q with confidence limits are calculated to form intuitive charts for engineers or operators to monitor the loss-of-main situation in real time. The effectiveness of the proposed methodology is evaluated on the loss-of-main monitoring of a real system, where the historic data are recorded from PMUs installed in several locations in the UK/Ireland power system.

Realist synthesis: Critical care rapid response systems.

Background Rapid Response Systems (RRS) consist of four interrelated and interdependent components; an event detection and trigger mechanism, a response strategy, a governance structure and process improvement system. These multiple components of the RRS pose problems in evaluation as the intervention is complex and cannot be evaluated using a traditional systematic review. Complex interventions in healthcare aimed at changing service delivery and related behaviour of health professionals require a different approach to summarising the evidence. Realist synthesis is such an approach to reviewing research evidence on complex interventions to provide an explanatory analysis of how and why an intervention works or doesn’t work in practice. The core principle is to make explicit the underlying assumptions about how an intervention is suppose to work (ie programme theory) and then use this theory to guide evaluation. Methods A realist synthesis process was used to explain those factors that enable or constrain the success of RRS programmes. Results The findings from the review include the articulation of the RRS programme theories, evaluation of whether these theories are supported or refuted by the research evidence and an evaluation of evidence to explain the underlying reasons why RRS works or doesn’t work in practice. Rival conjectured RRS programme theories were identified to explain the constraining factors regarding implementation of RRS in practice. These programme theories are presented using a logic model to highlight all the components which impact or influence the delivery of RRS programmes in the practice setting. The evidence from the realist synthesis provided the foundation for the development of hypothesis to test and refine the theories in the subsequent stages of the Realist Evaluation PhD study [1]. This information will be useful in providing evidence and direction for strategic and service planning of acute care to improve patient safety in hospital. References: McGaughey J, Blackwood B, O’Halloran P, Trinder T. J. & Porter S. (2010) Realistic Evaluation of Early Warning Systems and the Acute Life-threatening Events – Recognition and Treatment training course for early recognition and management of deteriorating ward-based patients: research protocol. Journal of Advanced Nursing 66 (4), 923-932.

Early warning systems: problems, pragmatics and potential

Symposium Chair: Dr Jennifer McGaughey

Title: Early Warning Systems: problems, pragmatics and potential

Early Warning Systems (EWS) provide a mechanism for staff to recognise, refer and manage deteriorating patients on general hospital wards. Implementation of EWS in practice has required considerable change in the delivery of critical care across hospitals. Drawing their experience of these changes the authors will demonstrate the problems and potential of using EWS to improve patient outcomes.

The first paper (Dr Jennifer McGaughey: Early Warning Systems: what works?) reviews the research evidence regarding the factors that support or constrain the implementation of Early Warning System (EWS) in practice. These findings explain those processes which impact on the successful achievement of patient outcomes. In order to improve detection and standardise practice National EWS have been implemented in the United Kingdom. The second paper (Catherine Plowright: The implementation of the National EWS in a District General Hospital) focuses on the process of implementing and auditing a National EWS. This process improvement is essential to contribute to future collaborative research and collection of robust datasets to improve patient safety as recommended by the Royal College of Physicians (RCP 2012). To successfully implement NEWS in practice requires strategic planning and staff education. The practical issues of training staff is discussed in the third paper. This paper (Collette Laws-Chapman: Simulation as a modality to embed the use of Early Warning Systems) focuses on using simulation and structured debrief to enhance learning in the early recognition and management of deteriorating patients. This session emphasises the importance of cognitive and social skills developed alongside practical skills in the simulated setting.

Star-planet interactions and selection effects from planet detection methods

Planets may have effects on their host stars by tidal or magnetic interaction. Such star-planet interactions are thought to enhance the activity level of the host star. However, stellar activity also affects the sensitivity of planet detection methods. Samples of planet-hosting stars which are investigated for such star-planet interactions are therefore subject to strong selection effects which need to be taken into account.

Contextual Intrusion Alerts for SCADA Networks

The complexity of modern SCADA networks and their associated cyber-attacks requires an expressive but flexible manner for representing both domain knowledge and collected intrusion alerts with the ability to integrate them for enhanced analytical capabilities and better understanding of attacks. This paper proposes an ontology-based approach for contextualized intrusion alerts in SCADA networks. In this approach, three security ontologies were developed to represent and store information on intrusion alerts, Modbus communications, and Modbus attack descriptions. This information is correlated into enriched intrusion alerts using simple ontology logic rules written in Semantic Query-Enhanced Web Rules (SQWRL). The contextualized alerts give analysts the means to better understand evolving attacks and to uncover the semantic relationships between sequences of individual attack events. The proposed system is illustrated by two use case scenarios.

Development of a Novel Assay for the Detection of Active Neutrophil Elastase in Patients with Chronic Obstructive Pulmonary Disease

Neutrophil elastase (NE), a biomarker of infection and inflammation, correlates with the severity of several respiratory diseases including chronic obstructive pulmonary disease (COPD). However, it’s detection and quantification in biological samples is confounded by a lack of reliable and robust methodologies. Standard assays using chromogenic or fluorogenic substrates are not specific when added to complex clinical samples containing multiple proteolytic and hydrolytic enzymes which have the ability to hydrolyse the substrate, thereby resulting in an over-estimation of the target protease. Furthermore, ELISA systems measure total protease levels which can be a mixture of latent, active and protease-inhibitor complexes. Therefore, we have developed a novel immunoassay (ProteaseTag™ Active NE Immunoassay) which is selective and specific for the capture of active NE in sputum and Bronchoalveolar Lavage (BAL) in patients with COPD. The objective of this study was to clinically validate ProteaseTag™ Active NE Ultra Immunoassay for the detection of NE in sputum from COPD patients. 20 matched sputum sol samples were collected from 10 COPD patients (M=6, F=4; 73 ± 6 years) during stable and exacerbation phases. Samples were assayed for NE activity utilising both ProteaseTag™ Active NE Ultra Immunoassay and a fluorogenic substrate-based kinetic activity assay. Both assays detected elevated levels of NE in the majority of patients (n=7) during an exacerbation (mean=217.2 μg/ml ±296.6) compared to their stable phase (mean=92.37 μg/ml ±259.8). However, statistical analysis did not show this difference to be significant (p=0.07, ProteaseTag™ Active NE Ultra Immunoassay; p=0.06 kinetic assay), most likely due to the low study number. A highly significant correlation was found between the 2 assay types (p≤0.0001, r=0.996). NE as a primary efficacy endpoint in clinical trials or as a marker of inflammation within the clinic has been hampered by the lack of a robust and simple to use assay. ProteaseTag™ Active NE Immunoassay specifically measures only active NE in clinical samples, is quick and easy to use (< 3 hours) and has no dependency on a kinetic readout. ProteaseTag™ technology is currently being transferred to a lateral flow device for use at Point of Care.

LS-ADT: Lightweight and Scalable Anomaly Detection for Cloud Datacentres

Cloud data centres are implemented as large-scale clusters with demanding requirements for service performance, availability and cost of operation. As a result of scale and complexity, data centres typically exhibit large numbers of system anomalies resulting from operator error, resource over/under provisioning, hardware or software failures and security issus anomalies are inherently difficult to identify and resolve promptly via human inspection. Therefore, it is vital in a cloud system to have automatic system monitoring that detects potential anomalies and identifies their source. In this paper we present a lightweight anomaly detection tool for Cloud data centres which combines extended log analysis and rigorous correlation of system metrics, implemented by an efficient correlation algorithm which does not require training or complex infrastructure set up. The LADT algorithm is based on the premise that there is a strong correlation between node level and VM level metrics in a cloud system. This correlation will drop significantly in the event of any performance anomaly at the node-level and a continuous drop in the correlation can indicate the presence of a true anomaly in the node. The log analysis of LADT assists in determining whether the correlation drop could be caused by naturally occurring cloud management activity such as VM migration, creation, suspension, termination or resizing. In this way, any potential anomaly alerts are reasoned about to prevent false positives that could be caused by the cloud operator’s activity. We demonstrate LADT with log analysis in a Cloud environment to show how the log analysis is combined with the correlation of systems metrics to achieve accurate anomaly detection.

A validated UPLC–MS/MS method for the surveillance of ten aquatic biotoxins in European brackish and freshwater systems

Over the past few decades, there has been an increased frequency and duration of cyanobacterial Harmful Algal Blooms (HABs) in freshwater systems globally. These can produce secondary metabolites called cyanotoxins, many of which are hepatotoxins, raising concerns about repeated exposure through ingestion of contaminated drinking water or food or through recreational activities such as bathing/ swimming. An ultra-performance liquid chromatography tandem mass spectrometry (UPLC–MS/MS) multi-toxin method has been developed and validated for freshwater cyanotoxins; microcystins-LR, -YR, -RR, -LA, -LY and -LF, nodularin, cylindrospermopsin, anatoxin-a and the marine diatom toxin domoic acid. Separation was achieved in around 9 min and dual SPE was incorporated providing detection limits of between 0.3 and 5.6 ng/L of original sample. Intra- and inter-day precision analysis showed relative
standard deviations (RSD) of 1.2–9.6% and 1.3–12.0% respectively. The method was applied to the analysis of aquatic samples (n = 206) from six European countries. The main class detected were the hepatotoxins; microcystin-YR (n = 22), cylindrospermopsin (n = 25), microcystin-RR (n = 17), microcystin-LR (n = 12), microcystin-LY (n = 1), microcystin-LF (n = 1) and nodularin (n = 5). For microcystins, the levels detected ranged from 0.001 to 1.51 mg/L, with two samples showing combined levels above the guideline set by the WHO of 1 mg/L for microcystin-LR. Several samples presented with multiple toxins indicating the potential for synergistic effects and possibly enhanced toxicity. This is the first published pan European survey of freshwater bodies for multiple biotoxins, including two identified for the first time; cylindrospermopsin in Ireland and nodularin in Germany, presenting further incentives for improved monitoring and development of strategies to mitigate human exposure.

Threshold Optimization for Energy Detection-Based Spectrum Sensing Over Hyper-Rayleigh Fading Channels

Spectrum sensing is a key function of cognitive radio systems. Sensing performance is determined by three main factors including the wireless channel between the primary system and the cognitive radio nodes, the detection threshold, and the sensing time. In this letter a closed-form expression for the average probability of detection for energy detection based spectrum sensing over two-wave with diffuse power fading channels is derived. This expression is then used to optimize the detection threshold for cognitive radio nodes, which operate in confined structures that exhibit worse than Rayleigh fading conditions. Such fading conditions can represent a behavioral model of cognitive machine-to-machine systems deployed in enclosed structures such as in-vehicular environments.