The iatrogenic reflex and belief that repression can coercively suppress asymmetric coercive violence : does very robust action by the government decrease functional support for insurgents?

Non-state insurgent actors are too weak to compel powerful adversaries to their will, so they use violence to coerce. A principal objective is to grow and sustain violent resistance to the point that it either militarily challenges the state, or more commonly, generates unacceptable political costs. To survive, insurgents must shift popular support away from the state and to grow they must secure it. State actor policies and actions perceived as illegitimate and oppressive by the insurgent constituency can generate these shifts. A promising insurgent strategy is to attack states in ways that lead angry publics and leaders to discount the historically established risks and take flawed but popular decisions to use repressive measures. Such decisions may be enabled by a visceral belief in the power of coercion and selective use of examples of where robust measures have indeed suppressed resistance. To avoid such counterproductive behaviours the cases of apparent 'successful repression' must be understood. This thesis tests whether robust state action is correlated with reduced support for insurgents, analyses the causal mechanisms of such shifts and examines whether such reduction is because of compulsion or coercion? The approach is founded on prior research by the RAND Corporation which analysed the 30 insurgencies most recently resolved worldwide to determine factors of counterinsurgent success. This new study first re-analyses their data at a finer resolution with new queries that investigate the relationship between repression and insurgent active support. Having determined that, in general, repression does not correlate with decreased insurgent support, this study then analyses two cases in which the data suggests repression seems likely to be reducing insurgent support: the PKK in Turkey and the insurgency against the Vietnamese-sponsored regime after their ousting of the Khmer Rouge. It applies 'structured-focused' case analysis with questions partly built from the insurgency model of Leites and Wolf, who are associated with the advocacy of US robust means in Vietnam. This is thus a test of 'most difficult' cases using a 'least likely' test model. Nevertheless, the findings refute the deterrence argument of 'iron fist' advocates. Robust approaches may physically prevent effective support of insurgents but they do not coercively deter people from being willing to actively support the insurgency.

Bayesian hierarchical models in statistical quality control methods to improve healthcare in hospitals

Quality oriented management systems and methods have become the dominant business and governance paradigm. From this perspective, satisfying customers’ expectations by supplying reliable, good quality products and services is the key factor for an organization and even government. During recent decades, Statistical Quality Control (SQC) methods have been developed as the technical core of quality management and continuous improvement philosophy and now are being applied widely to improve the quality of products and services in industrial and business sectors. Recently SQC tools, in particular quality control charts, have been used in healthcare surveillance. In some cases, these tools have been modified and developed to better suit the health sector characteristics and needs. It seems that some of the work in the healthcare area has evolved independently of the development of industrial statistical process control methods. Therefore analysing and comparing paradigms and the characteristics of quality control charts and techniques across the different sectors presents some opportunities for transferring knowledge and future development in each sectors. Meanwhile considering capabilities of Bayesian approach particularly Bayesian hierarchical models and computational techniques in which all uncertainty are expressed as a structure of probability, facilitates decision making and cost-effectiveness analyses. Therefore, this research investigates the use of quality improvement cycle in a health vii setting using clinical data from a hospital. The need of clinical data for monitoring purposes is investigated in two aspects. A framework and appropriate tools from the industrial context are proposed and applied to evaluate and improve data quality in available datasets and data flow; then a data capturing algorithm using Bayesian decision making methods is developed to determine economical sample size for statistical analyses within the quality improvement cycle. Following ensuring clinical data quality, some characteristics of control charts in the health context including the necessity of monitoring attribute data and correlated quality characteristics are considered. To this end, multivariate control charts from an industrial context are adapted to monitor radiation delivered to patients undergoing diagnostic coronary angiogram and various risk-adjusted control charts are constructed and investigated in monitoring binary outcomes of clinical interventions as well as postintervention survival time. Meanwhile, adoption of a Bayesian approach is proposed as a new framework in estimation of change point following control chart’s signal. This estimate aims to facilitate root causes efforts in quality improvement cycle since it cuts the search for the potential causes of detected changes to a tighter time-frame prior to the signal. This approach enables us to obtain highly informative estimates for change point parameters since probability distribution based results are obtained. Using Bayesian hierarchical models and Markov chain Monte Carlo computational methods, Bayesian estimators of the time and the magnitude of various change scenarios including step change, linear trend and multiple change in a Poisson process are developed and investigated. The benefits of change point investigation is revisited and promoted in monitoring hospital outcomes where the developed Bayesian estimator reports the true time of the shifts, compared to priori known causes, detected by control charts in monitoring rate of excess usage of blood products and major adverse events during and after cardiac surgery in a local hospital. The development of the Bayesian change point estimators are then followed in a healthcare surveillances for processes in which pre-intervention characteristics of patients are viii affecting the outcomes. In this setting, at first, the Bayesian estimator is extended to capture the patient mix, covariates, through risk models underlying risk-adjusted control charts. Variations of the estimator are developed to estimate the true time of step changes and linear trends in odds ratio of intensive care unit outcomes in a local hospital. Secondly, the Bayesian estimator is extended to identify the time of a shift in mean survival time after a clinical intervention which is being monitored by riskadjusted survival time control charts. In this context, the survival time after a clinical intervention is also affected by patient mix and the survival function is constructed using survival prediction model. The simulation study undertaken in each research component and obtained results highly recommend the developed Bayesian estimators as a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances as well as industrial and business contexts. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The empirical results and simulations indicate that the Bayesian estimators are a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The advantages of the Bayesian approach seen in general context of quality control may also be extended in the industrial and business domains where quality monitoring was initially developed.

A database for person re-identification in multi-camera surveillance networks

Person re-identification involves recognising individuals in different locations across a network of cameras and is a challenging task due to a large number of varying factors such as pose (both subject and camera) and ambient lighting conditions. Existing databases do not adequately capture these variations, making evaluations of proposed techniques difficult. In this paper, we present a new challenging multi-camera surveillance database designed for the task of person re-identification. This database consists of 150 unscripted sequences of subjects travelling in a building environment though up to eight camera views, appearing from various angles and in varying illumination conditions. A flexible XML-based evaluation protocol is provided to allow a highly configurable evaluation setup, enabling a variety of scenarios relating to pose and lighting conditions to be evaluated. A baseline person re-identification system consisting of colour, height and texture models is demonstrated on this database.

Context-aware multicast protocol for emergency message dissemination in vehicular networks

Road traffic accidents can be reduced by providing early warning to drivers through wireless ad hoc networks. When a vehicle detects an event that may lead to an imminent accident, the vehicle disseminates emergency messages to alert other vehicles that may be endangered by the accident. In many existing broadcast-based dissemination schemes, emergency messages may be sent to a large number of vehicles in the area and can be propagated to only one direction. This paper presents a more efficient context aware multicast protocol that disseminates messages only to endangered vehicles that may be affected by the emergency event. The endangered vehicles can be identified by calculating the interaction among vehicles based on their motion properties. To ensure fast delivery, the dissemination follows a routing path obtained by computing a minimum delay tree. The multicast protocol uses a generalized approach that can support any arbitrary road topology. The performance of the multicast protocol is compared with existing broadcast protocols by simulating chain collision accidents on a typical highway. Simulation results show that the multicast protocol outperforms the other protocols in terms of reliability, efficiency, and latency.

Langevin dynamics modeling of the water diffusion tensor in partially aligned collagen networks

In this work, a Langevin dynamics model of the diffusion of water in articular cartilage was developed. Numerical simulations of the translational dynamics of water molecules and their interaction with collagen fibers were used to study the quantitative relationship between the organization of the collagen fiber network and the diffusion tensor of water in model cartilage. Langevin dynamics was used to simulate water diffusion in both ordered and partially disordered cartilage models. In addition, an analytical approach was developed to estimate the diffusion tensor for a network comprising a given distribution of fiber orientations. The key findings are that (1) an approximately linear relationship was observed between collagen volume fraction and the fractional anisotropy of the diffusion tensor in fiber networks of a given degree of alignment, (2) for any given fiber volume fraction, fractional anisotropy follows a fiber alignment dependency similar to the square of the second Legendre polynomial of cos(θ), with the minimum anisotropy occurring at approximately the magic angle (θMA), and (3) a decrease in the principal eigenvalue and an increase in the transverse eigenvalues is observed as the fiber orientation angle θ progresses from 0◦ to 90◦. The corresponding diffusion ellipsoids are prolate for θ < θMA, spherical for θ ≈ θMA, and oblate for θ > θMA. Expansion of the model to include discrimination between the combined effects of alignment disorder and collagen fiber volume fraction on the diffusion tensor is discussed.

The relationship between Bayesian motor unit number estimation and histological measurements of motor neurons in wild-type and SOD1 G93A mice

Objective: To assess the relationship between Bayesian MUNE and histological motor neuron counts in wild-type mice and in an animal model of ALS. Methods: We performed Bayesian MUNE paired with histological counts of motor neurons in the lumbar spinal cord of wild-type mice and transgenic SOD1 G93A mice that show progressive weakness over time. We evaluated the number of acetylcholine endplates that were innervated by a presynaptic nerve. Results: In wild-type mice, the motor unit number in the gastrocnemius muscle estimated by Bayesian MUNE was approximately half the number of motor neurons in the region of the spinal cord that contains the cell bodies of the motor neurons supplying the hindlimb crural flexor muscles. In SOD1 G93A mice, motor neuron numbers declined over time. This was associated with motor endplate denervation at the end-stage of disease. Conclusion: The number of motor neurons in the spinal cord of wild-type mice is proportional to the number of motor units estimated by Bayesian MUNE. In SOD1 G93A mice, there is a lower number of estimated motor units compared to the number of spinal cord motor neurons at the end-stage of disease, and this is associated with disruption of the neuromuscular junction. Significance: Our finding that the Bayesian MUNE method gives estimates of motor unit numbers that are proportional to the numbers of motor neurons in the spinal cord supports the clinical use of Bayesian MUNE in monitoring motor unit loss in ALS patients. © 2012 International Federation of Clinical Neurophysiology.

Bayesian experimental design for models with intractable likelihoods

In this paper we present a methodology for designing experiments for efficiently estimating the parameters of models with computationally intractable likelihoods. The approach combines a commonly used methodology for robust experimental design, based on Markov chain Monte Carlo sampling, with approximate Bayesian computation (ABC) to ensure that no likelihood evaluations are required. The utility function considered for precise parameter estimation is based upon the precision of the ABC posterior distribution, which we form efficiently via the ABC rejection algorithm based on pre-computed model simulations. Our focus is on stochastic models and, in particular, we investigate the methodology for Markov process models of epidemics and macroparasite population evolution. The macroparasite example involves a multivariate process and we assess the loss of information from not observing all variables.

Analyzing the factors influencing the successful design and uptake of interactive systems to support social networks in urban neighborhoods

In urban residential environments in Australia and other developed countries, Internet access is on the verge of becoming a ubiquitous utility like gas or electricity. From an urban sociology and community informatics perspective, this article discusses new emerging social formations of urban residents that are based on networked individualism and the potential of Internet-based systems to support them. It proposes that one of the main reasons for the disappearance or nonexistence of urban residential communities is a lack of appropriate opportunities and instruments to encourage and support local interaction in urban neighborhoods. The article challenges the view that a mere reappropriation of applications used to support dispersed virtual communities is adequate to meet the place and proximity-based design requirements that community networks in urban neighborhoods pose. It argues that the key factors influencing the successful design and uptake of interactive systems to support social networks in urban neighborhoods include the swarming social behavior of urban dwellers; the dynamics of their existing communicative ecology; and the serendipitous, voluntary, and place-based quality of interaction between residents on the basis of choice, like-mindedness, mutual interest and support needs. Drawing on an analysis of these factors, the conceptual design framework of a prototype system — the urban tribe incubator — is presented.

Communication and organizational social networks : a simulation model

Recent management research has evidenced the significance of organizational social networks, and communication is believed to impact the interpersonal relationships. However, we have little knowledge on how communication affects organizational social networks. This paper studies the dynamics between organizational communication patterns and the growth of organizational social networks. We propose an organizational social network growth model, and then collect empirical data to test model validity. The simulation results agree well with the empirical data. The results of simulation experiments enrich our knowledge on communication with the findings that organizational management practices that discourage employees from communicating within and across group boundaries have disparate and significant negative effect on the social network’s density, scalar assortativity and discrete assortativity, each of which correlates with the organization’s performance. These findings also suggest concrete measures for management to construct and develop the organizational social network.

Dalcetrapib restoring belief in modulating CETP as a beneficial mechanism in cardiovascular disease

Abstract Background: As low HDL cholesterol levels are a risk factor for cardiovascular disease, raising HDL cholesterol substantially by inhibiting or modulating cholesteryl ester transfer protein (CETP) may be useful in coronary artery disease. The first CETP inhibitor that went into clinical trial, torcetrapib, was shown to increase the levels of HDL cholesterol, but it also increased cardiovascular outcomes, probably due to an increase in blood pressure and aldosterone secretion, by an off-target mechanism/s. Objective/methods: Dalcetrapib is a new CETP modulator that increases the levels of HDL cholesterol, but does not increase blood pressure or aldosterone secretion. The objective was to evaluate a paper describing the effects of dalcetrapib on carotid and aortic wall thickness in subjects with, or at high risk, of coronary artery disease; the dal-PLAQUE study. Results: dal-PLAQUE showed that dalcetrapib reduced the progression of atherosclerosis and may also reduce the vascular inflammation associated with this, in subjects with, or with high risk of, coronary heart disease, who were already taking statins. Conclusions: These results suggest that modulating CETP with dalcetrapib may be a beneficial mechanism in cardiovascular disease. The results of the dal-HEART series, which includes dal-PLAQUE 1 and 2, and dal-OUTCOMES, when complete, will provide more definitive information about the benefit, or not, of dalcetrapib in coronary artery disease.

Performance analysis of IEC 61850 sampled value process bus networks

Process bus networks are the next stage in the evolution of substation design, bringing digital technology to the high voltage switchyard. Benefits of process buses include facilitating the use of Non-Conventional Instrument Transformers, improved disturbance recording and phasor measurement and the removal of costly, and potentially hazardous, copper cabling from substation switchyards and control rooms. This paper examines the role a process bus plays in an IEC 61850 based Substation Automation System. Measurements taken from a process bus substation are used to develop an understanding of the network characteristics of "whole of substation" process buses. The concept of "coherent transmission" is presented and the impact of this on Ethernet switches is examined. Experiments based on substation observations are used to investigate in detail the behavior of Ethernet switches with sampled value traffic. Test methods that can be used to assess the adequacy of a network are proposed, and examples of the application and interpretation of these tests are provided. Once sampled value frames are queued by an Ethernet switch the additional delay incurred by subsequent switches is minimal, and this allows their use in switchyards to further reduce communications cabling, without significantly impacting operation. The performance and reliability of a process bus network operating with close to the theoretical maximum number of digital sampling units (merging units or electronic instrument transformers) was investigated with networking equipment from several vendors, and has been demonstrated to be acceptable.

The governance of inter-organisational decision-making : understanding the impacts of informal networks on formal decision-making regimes in the Brisbane Airport region

Airports and cities inevitably recognise the value that each brings the other; however, the separation in decision-making authority for what to build, where, when and how provides a conundrum for both parties. Airports often want a say in what is developed outside of the airport fence, and cities often want a say in what is developed inside the airport fence. Defining how much of a say airports and cities have in decisions beyond their jurisdictional control is likely to be a topic that continues so long as airports and cities maintain separate formal decision-making processes for what to build, where, when and how. However, the recent Green and White Papers for a new National Aviation Policy have made early inroads to formalising relationships between Australia’s major airports and their host cities. At present, no clear indication (within practice or literature) is evident to the appropriateness of different governance arrangements for decisions to develop in situations that bring together the opposing strategic interests of airports and cities; thus leaving decisions for infrastructure development as complex decision-making spaces that hold airport and city/regional interests at stake. The line of enquiry is motivated by a lack of empirical research on networked decision-making domains outside of the realm of institutional theorists (Agranoff & McGuire, 2001; Provan, Fish & Sydow, 2007). That is, governance literature has remained focused towards abstract conceptualisations of organisation, without focusing on the minutia of how organisation influences action in real-world applications. A recent study by Black (2008) has provided an initial foothold for governance researchers into networked decision-making domains. This study builds upon Black’s (2008) work by aiming to explore and understand the problem space of making decisions subjected to complex jurisdictional and relational interdependencies. That is, the research examines the formal and informal structures, relationships, and forums that operationalise debates and interactions between decision-making actors as they vie for influence over deciding what to build, where, when and how in airport-proximal development projects. The research mobilises a mixture of qualitative and quantitative methods to examine three embedded cases of airport-proximal development from a network governance perspective. Findings from the research provide a new understanding to the ways in which informal actor networks underpin and combine with formal decision-making networks to create new (or realigned) governance spaces that facilitate decision-making during complex phases of development planning. The research is timely, and responds well to Isett, Mergel, LeRoux, Mischen and Rethemeyer’s (2011) recent critique of limitations within current network governance literature, specifically to their noted absence of empirical studies that acknowledge and interrogate the simultaneity of formal and informal network structures within network governance arrangements (Isett et al., 2011, pp. 162-166). The combination of social network analysis (SNA) techniques and thematic enquiry has enabled findings to document and interpret the ways in which decision-making actors organise to overcome complex problems for planning infrastructure. An innovative approach to using association networks has been used to provide insights to the importance of the different ways actors interact with one another, thus providing a simple yet valuable addition to the increasingly popular discipline of SNA. The research also identifies when and how different types of networks (i.e. formal and informal) are able to overcome currently known limitations to network governance (see McGuire & Agranoff, 2011), thus adding depth to the emerging body of network governance literature surrounding limitations to network ways of working (i.e. Rhodes, 1997a; Keast & Brown, 2002; Rethemeyer & Hatmaker, 2008; McGuire & Agranoff, 2011). Contributions are made to practice via the provision of a timely understanding of how horizontal fora between airports and their regions are used, particularly in the context of how they reframe the governance of decision-making for airport-proximal infrastructure development. This new understanding will enable government and industry actors to better understand the structural impacts of governance arrangements before they design or adopt them, particularly for factors such as efficiency of information, oversight, and responsiveness to change.

Talking of many things : using topical networks to study discussions in social media

This paper outlines a method for studying online activity using both qualitative and quantitative methods: topical network analysis. A topical network refers to "the collection of sites commenting on a particular event or issue, and the links between them" (Highfield, Kirchhoff, & Nicolai, 2011, p. 341). The approach is a complement for the analysis of large datasets enabling the examination and comparison of different discussions as a means of improving our understanding of the uses of social media and other forms of online communication. Developed for an analysis of political blogging, the method also has wider applications for other social media websites such as Twitter.

Eigenvoice modeling for cross likelihood ratio based speaker clustering : a Bayesian approach

This paper proposes the use of Bayesian approaches with the cross likelihood ratio (CLR) as a criterion for speaker clustering within a speaker diarization system, using eigenvoice modeling techniques. The CLR has previously been shown to be an effective decision criterion for speaker clustering using Gaussian mixture models. Recently, eigenvoice modeling has become an increasingly popular technique, due to its ability to adequately represent a speaker based on sparse training data, as well as to provide an improved capture of differences in speaker characteristics. The integration of eigenvoice modeling into the CLR framework to capitalize on the advantage of both techniques has also been shown to be beneficial for the speaker clustering task. Building on that success, this paper proposes the use of Bayesian methods to compute the conditional probabilities in computing the CLR, thus effectively combining the eigenvoice-CLR framework with the advantages of a Bayesian approach to the diarization problem. Results obtained on the 2002 Rich Transcription (RT-02) Evaluation dataset show an improved clustering performance, resulting in a 33.5% relative improvement in the overall Diarization Error Rate (DER) compared to the baseline system.