Noninvasive techniques in assessing coronary artery disease

Conventional invasive coronary angiography is the clinical gold standard for detecting of coronary artery stenoses. Noninvasive multidetector computed tomography (MDCT) in combination with retrospective ECG gating has recently been shown to permit visualization of the coronary artery lumen and detection of coronary artery stenoses. Single photon emission tomography (SPECT) perfusion imaging has been considered the reference method for evaluation of nonviable myocardium, but magnetic resonance imaging (MRI) can accurately depict structure, function, effusion, and myocardial viability, with an overall capacity unmatched by any other single imaging modality. Magnetocardiography (MCG) provides noninvasively information about myocardial excitation propagation and repolarization without the use of electrodes. This evolving technique may be considered the magnetic equivalent to electrocardiography. The aim of the present series of studies was to evaluate changes in the myocardium assessed with SPECT and MRI caused by coronary artery disease, examine the capability of multidetector computed tomography coronary angiography (MDCT-CA) to detect significant stenoses in the coronary arteries, and MCG to assess remote myocardial infarctions. Our study showed that in severe, progressing coronary artery disease laser treatment does not improve global left ventricular function or myocardial perfusion, but it does preserve systolic wall thickening in fixed defects (scar). It also prevents changes from ischemic myocardial regions to scar. The MCG repolarization variables are informative in remote myocardial infarction, and may perform as well as the conventional QRS criteria in detection of healed myocardial infarction. These STT abnormalities are more pronounced in patients with Q-wave infarction than in patients with non-Q-wave infarctions. MDCT-CA had a sensitivity of 82%, a specificity of 94%, a positive predictive value of 79%, and a negative predictive value of 95% for stenoses over 50% in the main coronary arteries as compared with conventional coronary angiography in patients with known coronary artery disease. Left ventricular wall dysfunction, perfusion defects, and infarctions were detected in 50-78% of sectors assigned to calcifications or stenoses, but also in sectors supplied by normally perfused coronary arteries. Our study showed a low sensitivity (sensitivity 63%) in detecting obstructive coronary artery disease assessed by MDCT in patients with severe aortic stenosis. Massive calcifications complicated correct assessment of the lumen of coronary arteries.

Urban satellite image classification using biologically inspired techniques

This paper focuses on optimisation algorithms inspired by swarm intelligence for satellite image classification from high resolution satellite multi- spectral images. Amongst the multiple benefits and uses of remote sensing, one of the most important has been its use in solving the problem of land cover mapping. As the frontiers of space technology advance, the knowledge derived from the satellite data has also grown in sophistication. Image classification forms the core of the solution to the land cover mapping problem. No single classifier can prove to satisfactorily classify all the basic land cover classes of an urban region. In both supervised and unsupervised classification methods, the evolutionary algorithms are not exploited to their full potential. This work tackles the land map covering by Ant Colony Optimisation (ACO) and Particle Swarm Optimisation (PSO) which are arguably the most popular algorithms in this category. We present the results of classification techniques using swarm intelligence for the problem of land cover mapping for an urban region. The high resolution Quick-bird data has been used for the experiments.

Optimisation techniques for planning automatic under frequency load shedding in New Zealand’s power system

Electricity generation is vital in developed countries to power the many mechanical and electrical devices that people require. Unfortunately electricity generation is costly. Though electricity can be generated it cannot be stored efficiently. Electricity generation is also difficult to manage because exact demand is unknown from one instant to the next. A number of services are required to manage fluctuations in electricity demand, and to protect the system when frequency falls too low. A current approach is called automatic under frequency load shedding (AUFLS). This article proposes new methods for optimising AUFLS in New Zealand’s power system. The core ideas were developed during the 2015 Maths and Industry Study Group (MISG) in Brisbane, Australia. The problem has been motivated by Transpower Limited, a company that manages New Zealand’s power system and transports bulk electricity from where it is generated to where it is needed. The approaches developed in this article can be used in electrical power systems anywhere in the world.

Quadrature generation techniques for frequency multiplication based oscillators

Frequency multiplication (FM) can be used to design low power frequency synthesizers. This is achieved by running the VCO at a much reduced frequency, while employing a power efficient frequency multiplier, and also thereby eliminating the first few dividers. Quadrature signals can be generated by frequency- multiplying low frequency I/Q signals, however this also multiplies the quadrature error of these signals. Another way is generating additional edges from the low-frequency oscillator (LFO) and develop a quadrature FM. This makes the I-Q precision heavily dependent on process mismatches in the ring oscillator. In this paper we examine the use of fewer edges from LFO and a single stage polyphase filter to generate approximate quadrature signals, which is then followed by an injection-locked quadrature VCO to generate high- precision I/Q signals. Simulation comparisons with the existing approach shows that the proposed method offers very good phase accuracy of 0.5deg with only a modest increase in power dissipation for 2.4 GHz IEEE 802.15.4 standard using UMC 0.13 mum RFCMOS technology.

Predicting crashes using traffic offences. A meta-analysis that examines potential bias between self-report and archival data

Background Traffic offences have been considered an important predictor of crash involvement, and have often been used as a proxy safety variable for crashes. However the association between crashes and offences has never been meta-analysed and the population effect size never established. Research is yet to determine the extent to which this relationship may be spuriously inflated through systematic measurement error, with obvious implications for researchers endeavouring to accurately identify salient factors predictive of crashes. Methodology and Principal Findings Studies yielding a correlation between crashes and traffic offences were collated and a meta-analysis of 144 effects drawn from 99 road safety studies conducted. Potential impact of factors such as age, time period, crash and offence rates, crash severity and data type, sourced from either self-report surveys or archival records, were considered and discussed. After weighting for sample size, an average correlation of r = .18 was observed over the mean time period of 3.2 years. Evidence emerged suggesting the strength of this correlation is decreasing over time. Stronger correlations between crashes and offences were generally found in studies involving younger drivers. Consistent with common method variance effects, a within country analysis found stronger effect sizes in self-reported data even controlling for crash mean. Significance The effectiveness of traffic offences as a proxy for crashes may be limited. Inclusion of elements such as independently validated crash and offence histories or accurate measures of exposure to the road would facilitate a better understanding of the factors that influence crash involvement.

Space-Vector-Based Hybrid Pulsewidth Modulation Techniques for Reduced Harmonic Distortion and Switching Loss

Novel switching sequences can be employed in spacevector-based pulsewidth modulation (PWM) of voltage source inverters. Differentswitching sequences are evaluated and compared in terms of inverter switching loss. A hybrid PWM technique named minimum switching loss PWM is proposed, which reduces the inverter switching loss compared to conventional space vector PWM (CSVPWM) and discontinuous PWM techniques at a given average switching frequency. Further, four space-vector-based hybrid PWM techniques are proposed that reduce line current distortion as well as switching loss in motor drives, compared to CSVPWM. Theoretical and experimental results are presented.

Supervisory Control for Real-Time Systems Based on Conflict-Tolerant Controllers

This paper addresses the problem of detecting and resolving conflicts due to timing constraints imposed by features in real-time and hybrid systems. We consider systems composed of a base system with multiple features or controllers, each of which independently advise the system on how to react to input events so as to conform to their individual specifications. We propose a methodology for developing such systems in a modular manner based on the notion of conflict-tolerant features that are designed to continue offering advice even when their advice has been overridden in the past. We give a simple priority-based scheme forcomposing such features. This guarantees the maximal use of each feature. We provide a formal framework for specifying such features, and a compositional technique for verifying systems developed in this framework.

Optimal Resource Partitioning in a Military Conflict based on Lanchester Attrition Models

This paper develops a model for military conflicts where the defending forces have to determine an optimal partitioning of available resources to counter attacks from an adversary in two different fronts. The Lanchester attrition model is used to develop the dynamical equations governing the variation in force strength. Three different allocation schemes - Time-Zero-Allocation (TZA), Allocate-Assess-Reallocate (AAR), and Continuous Constant Allocation (CCA) - are considered and the optimal solutions are obtained in each case. Numerical examples are given to support the analytical results.

Disarming identities : Ethnic identities in conflict resolution: Hindrance or helpful resource?

This thesis proposes that national or ethnic identity is an important and overlooked resource in conflict resolution. Usually ethnic identity is seen both in international relations and in social psychology as something that fuels the conflict. Using grounded theory to analyze data from interactive problem-solving workshops between Palestinians and Israelis a theory about the role of national identity in turning conflict into protracted conflict is developed. Drawing upon research from, among others, social identity theory, just world theory and prejudice it is argued that national identity is a prime candidate to provide the justification of a conflict party’s goals and the dehumanization of the other necessary to make a conflict protracted. It is not the nature of national identity itself that lets it perform this role but rather the ability to mobilize a constituency for social action (see Stürmer, Simon, Loewy, & Jörger, 2003). Reicher & Hopkins (1996) have demonstrated that national identity is constructed by political entrepreneurs to further their cause, even if this construction is not a conscious one. Data from interactive problem-solving workshops suggest that the possibility of conflict resolution is actually seen by participants as a direct threat of annihilation. Understanding the investment necessary to make conflict protracted this reaction seems plausible. The justification for ones actions provided by national identity makes the conflict an integral part of a conflict party’s identity. Conflict resolution, it is argued, is therefore a threat to the very core of the current national identity. This may explain why so many peace agreements have failed to provide the hoped for resolution of conflict. But if national identity is being used in a constructionist way to attain political goals, a political project of conflict resolution, if it is conscious of the constructionist process, needs to develop a national identity that is independent of conflict and therefore able to accommodate conflict resolution. From this understanding it becomes clear why national identity needs to change, i.e. be disarmed, if conflict resolution is to be successful. This process of disarmament is theorized to be similar to the process of creating and sustaining protracted conflict. What shape and function this change should have is explored from the understanding of the role of national identity in supporting conflict. Ideas how track-two diplomacy efforts, such as the interactive problem-solving workshop, could integrate a process by both conflict parties to disarm their respective identities are developed.

Optimal Resource Partitioning in a Military Conflict based on Lanchester Attrition Models

This paper develops a model for military conflicts where the defending forces have to determine an optimal partitioning of available resources to counter attacks from an adversary in two different fronts. The Lanchester attrition model is used to develop the dynamical equations governing the variation in force strength. Three different allocation schemes - Time-Zero-Allocation (TZA), Allocate-Assess-Reallocate (AAR), and Continuous Constant Allocation (CCA) - are considered and the optimal solutions are obtained in each case. Numerical examples are given to support the analytical results.

Multivariate Techniques for Identifying Diffractive Interactions at the LHC

Close to one half of the LHC events are expected to be due to elastic or inelastic diffractive scattering. Still, predictions based on extrapolations of experimental data at lower energies differ by large factors in estimating the relative rate of diffractive event categories at the LHC energies. By identifying diffractive events, detailed studies on proton structure can be carried out. The combined forward physics objects: rapidity gaps, forward multiplicity and transverse energy flows can be used to efficiently classify proton-proton collisions. Data samples recorded by the forward detectors, with a simple extension, will allow first estimates of the single diffractive (SD), double diffractive (DD), central diffractive (CD), and non-diffractive (ND) cross sections. The approach, which uses the measurement of inelastic activity in forward and central detector systems, is complementary to the detection and measurement of leading beam-like protons. In this investigation, three different multivariate analysis approaches are assessed in classifying forward physics processes at the LHC. It is shown that with gene expression programming, neural networks and support vector machines, diffraction can be efficiently identified within a large sample of simulated proton-proton scattering events. The event characteristics are visualized by using the self-organizing map algorithm.