Fidelity bias in mollusk assemblages from coastal lagoons of Southern Brazil

The South America southern coast exhibits many outcrops with abundant shell beds, from the Pleistocene through the Recent. How much biological information is preserved within these shell beds? Or, what is the actual probability a living community has to leave a fossil record corresponding to these shell deposits? Although ecological and biogeographical aspects might had been pointed, considering these temporal scales, up to the moment there is no taphonomically-oriented studies available. Quantitative comparisons between living (LAs), death (DAs) and fossil assemblages (FAs) are important not only in strictly taphonomic studies, but have grown a leading tool for conservation paleobiology analysis. Comparing LAs, DAs and FAs from estuaries and lagoons in the Rio Grande do Sul Coastal Plain makes possible to quantitatively understand the nature and quantity of biological information preserved in fossil associations in Holocene lagoon facies. As already noted by several authors, spatial scale parts the analysis, but we detected that the FAs refl ects live ones, rather than dead ones, as previously not realized. The results herein obtained illustrates that species present in DA are not as good preserved in recent (Holocene) fossil record as originally thought. Strictly lagoon species are most prone to leave fossil record. The authors consider that the fi delity pattern here observed for estuarine mollusks to be driven by (i) high temporal and spatial variability in the LAs, (ii) spatial mixing in the DA and (iii) differential preservation of shells, due to long residence times in the taphonomically active zone.

Death Threat Letters: Allegories of American Authorship in the Age of Terrorism

This dissertation examines novels that use terrorism to allegorize the threatened position of the literary author in contemporary culture. Allegory is a term that has been differently understood over time, but which has consistently been used by writers to articulate and construct their roles as authors. In the novels I look at, the terrorist challenge to authorship results in multiple deployments of allegory, each differently illustrating the way that allegory is used and authorship constructed in the contemporary American novel. Don DeLillo’s Mao II (1991), first puts terrorists and authors in an oppositional pairing. The terrorist’s ability to traffic in spectacle is presented as indicative of the author’s fading importance in contemporary culture and it is one way that terrorism allegorizes threats to authorship. In Philip Roth’s Operation Shylock (1993), the allegorical pairing is between the text of the novel and outside texts – newspaper reports, legal cases, etc. – that the novel references and adapts in order to bolster its own narrative authority. Richard Powers’s Plowing the Dark (1999) pairs the story of an imprisoned hostage, craving a single book, with employees of a tech firm who are creating interactive, virtual reality artworks. Focusing on the reader’s experience, Powers’s novel posits a form of authorship that the reader can take into consideration, but which does not seek to control the experience of the text. Finally, I look at two of Paul Auster’s twenty-first century novels, Travels in the Scriptorium (2007) and Man in the Dark (2008), to suggest that the relationship between representations of authors and terrorists changed after 9/11. Auster’s author-figures forward an ethics of authorship whereby novels can use narrative to buffer readers against the portrayal of violent acts in a culture that is suffused with traumatizing imagery.

Towards a threat assessment framework for apps collusion

App collusion refers to two or more apps working together to achieve a malicious goal that they otherwise would not be able to achieve individually. The permissions based security model (PBSM) for Android does not address this threat, as it is rather limited to mitigating risks due to individual apps. This paper presents a technique for assessing the threat of collusion for apps, which is a first step towards quantifying collusion risk, and allows us to narrow down to candidate apps for collusion, which is critical given the high volume of Android apps available. We present our empirical analysis using a classified corpus of over 29000 Android apps provided by Intel Security.

A Bias Compensated Cross-Relation approach to Thermocouple Characterisation

The measurement of fast changing temperature fluctuations is a challenging problem due to the inherent limited bandwidth of temperature sensors. This results in a measured signal that is a lagged and attenuated version of the input. Compensation can be performed provided an accurate, parameterised sensor model is available. However, to account for the in influence of the measurement environment and changing conditions such as gas velocity, the model must be estimated in-situ. The cross-relation method of blind deconvolution is one approach for in-situ characterisation of sensors. However, a drawback with the method is that it becomes positively biased and unstable at high noise levels. In this paper, the cross-relation method is cast in the discrete-time domain and a bias compensation approach is developed. It is shown that the proposed compensation scheme is robust and yields unbiased estimates with lower estimation variance than the uncompensated version. All results are verified using Monte-Carlo simulations.

Threat Analysis of BlackEnergy Malware for Synchrophasor based Real-time Control and Monitoring in Smart Grid

The BlackEnergy malware targeting critical infrastructures has a long history. It evolved over time from a simple DDoS platform to a quite sophisticated plug-in based malware. The plug-in architecture has a persistent malware core with easily installable attack specific modules for DDoS, spamming, info-stealing, remote access, boot-sector formatting etc. BlackEnergy has been involved in several high profile cyber physical attacks including the recent Ukraine power grid attack in December 2015. This paper investigates the evolution of BlackEnergy and its cyber attack capabilities. It presents a basic cyber attack model used by BlackEnergy for targeting industrial control systems. In particular, the paper analyzes cyber threats of BlackEnergy for synchrophasor based systems which are used for real-time control and monitoring functionalities in smart grid. Several BlackEnergy based attack scenarios have been investigated by exploiting the vulnerabilities in two widely used synchrophasor communication standards: (i) IEEE C37.118 and (ii) IEC 61850-90-5. Specifically, the paper addresses reconnaissance, DDoS, man-in-the-middle and replay/reflection attacks on IEEE C37.118 and IEC 61850-90-5. Further, the paper also investigates protection strategies for detection and prevention of BlackEnergy based cyber physical attacks.

The effect of changing the built environment on physical activity: a quantitative review of the risk of bias in natural experiments.

BACKGROUND:
Evidence regarding the association of the built environment with physical activity is influencing policy recommendations that advocate changing the built environment to increase population-level physical activity. However, to date there has been no rigorous appraisal of the quality of the evidence on the effects of changing the built environment. The aim of this review was to conduct a thorough quantitative appraisal of the risk of bias present in those natural experiments with the strongest experimental designs for assessing the causal effects of the built environment on physical activity.

METHODS:
Eligible studies had to evaluate the effects of changing the built environment on physical activity, include at least one measurement before and one measurement of physical activity after changes in the environment, and have at least one intervention site and non-intervention comparison site. Given the large number of systematic reviews in this area, studies were identified from three exemplar systematic reviews; these were published in the past five years and were selected to provide a range of different built environment interventions. The risk of bias in these studies was analysed using the Cochrane Risk of Bias Assessment Tool: for Non-Randomized Studies of Interventions (ACROBAT-NRSI).

RESULTS:
Twelve eligible natural experiments were identified. Risk of bias assessments were conducted for each physical activity outcome from all studies, resulting in a total of fifteen outcomes being analysed. Intervention sites included parks, urban greenways/trails, bicycle lanes, paths, vacant lots, and a senior citizen's centre. All outcomes had an overall critical (n = 12) or serious (n = 3) risk of bias. Domains with the highest risk of bias were confounding (due to inadequate control sites and poor control of confounding variables), measurement of outcomes, and selection of the reported result.

CONCLUSIONS:
The present review focused on the strongest natural experiments conducted to date. Given this, the failure of existing studies to adequately control for potential sources of bias highlights the need for more rigorous research to underpin policy recommendations for changing the built environment to increase physical activity. Suggestions are proposed for how future natural experiments in this area can be improved.

A Bias Compensated Cross-Relation approach to Thermocouple Characterisation

The measurement of fast changing temperature fluctuations is a challenging problem due to the inherent limited bandwidth of temperature sensors. This results in a measured signal that is a lagged and attenuated version of the input. Compensation can be performed provided an accurate, parameterised sensor model is available. However, to account for the influence of the measurement environment and changing conditions such as gas velocity, the model must be estimated in-situ. The cross-relation method of blind deconvolution is one approach for in-situ characterisation of sensors. However, a drawback with the method is that it becomes positively biased and unstable at high noise levels. In this paper, the cross-relation method is cast in the discrete-time domain and a bias compensation approach is developed. It is shown that the proposed compensation scheme is robust and yields unbiased estimates with lower estimation variance than the uncompensated version. All results are verified using Monte-Carlo simulations.

Exchange-Bias-Dünnschichtsysteme - Charakterisierung, Modellierung und Anwendung

In dieser Arbeit werden Exchange-Bias-Dünnschichtsysteme untersucht. Ein Fokus der Arbeit liegt auf der magnetooptischen Aktivität dieser Systeme und im speziellen wie sich diese optimieren lässt. Der zweite Fokus der Arbeit ist die Modellierung dieser Systeme. Aufbauend auf einem polykristallinen Ansatz wird eine neue rotierbare magnetische Anisotropie eingeführt, welche die Relaxationszeiten thermisch instabiler Körner im AF berücksichtigt.

Memory Bias for Anxiety Prior to Academic Achievement Situations: Influences of Time, Mood, Personality and Post-Event-Processing

The current thesis examines memory bias for state anxiety prior to academic achievement situations like writing an exam and giving a speech. The thesis relies on the reconstruction principle, which assumes that memories for past emotions are reconstructed rather than stored permanently and accurately. This makes them prone to memory bias, which is af-fected by several influencing factors. A major aim is to include four important influencing factors simultaneously. Early research on mood and emotional autobiographical memory found evidence for the existence of a propositional associative network (Bower, 1981; Col-lins & Loftus, 1975), leading to mood congruent recall. But empirical findings gave also strong evidence for the existence of mood incongruent recall for one’s own emotions, which was for example linked to mood regulation via mood repair (e.g. Clark & Isen, 1982), which seems to be associated to the personality traits extraversion and neuroticism (Lischetzke & Eid, 2006; Ng & Diener, 2009). Moreover, neuroticism and trait anxiety are related to rumination, which is seen as negative post-event-processing (e.g. Wells & Clark, 1997). Overall, the elapsed time since the emotional event happened should have an impact on recall of emotions. Following the affect infusion model by Robinson and Clore (2002a), the influence of personality on memory bias should increase over time. Therefore, three longitudinal studies were realized, using naturally occurring as well as laboratory settings. The used paradigm was equivalent in all studies. Subjects were asked about their actual state anxiety prior to an academic achievement situation. Directly after the situation, cur-rent mood and recall of former anxiety were assessed. The same procedure was repeated a few weeks later. Personality traits and post-event-processing were also assessed. The results suggest a need to have a differentiated view on predicting memory bias. Study 1 (N = 131) as well as study 3 (N = 53) found evidence for mood incongruent memory in the sense of mood repair and downward regulation as a function of personality. Rumination was found to cause stable overestimation of pre-event anxiety in study 2 (N = 141) as well as in study 3. Although the relevance of the influencing factors changed over time, an increasing relevance of personality could not consistently be observed. The tremendously different effects of the laboratory study 2 indicated that such settings are not appropriate to study current issues. Theoretical and psychotherapeutically relevant conclusions are drawn and several limitations are discussed.

Black and Blue: Exploring Racial Bias and Law Enforcement in the Killings of Unarmed Black Male Civilians

In late 2014, a series of highly publicized police killings of unarmed Black male civilians in the United States prompted large-scale social turmoil. In the current review, we dissect the psychological antecedents of these killings and explain how the nature of police work may attract officers with distinct characteristics that may make them especially well-primed for negative interactions with Black male civilians. We use media reports to contextualize the precipitating events of the social unrest as we ground our explanations in theory and empirical research from social psychology and industrial and organizational (I/O) psychology. To isolate some of the key mechanisms at play, we disentangle racial bias (e.g., stereotyping processes) from common characteristics of law enforcement agents (e.g., social dominance orientation), while also addressing the interaction between racial bias and policing. By separating the moving parts of the phenomenon, we provide a more fine-grained analysis of the factors that may have contributed to the killings. In doing so, we endeavor to more effectively identify and develop solutions to eradicate excessive use of force during interactions between “Black” (unarmed Black male civilians) and “Blue” (law enforcement).

Quantifying Selection Bias from Birth History Estimates of Child Mortality

Thesis (Master's)--University of Washington, 2016-08

Publication Bias

According to the Declaration of Helsinki, as well as the Statement on Public Disclosure of Clinical Trial Results of the World Health Organization, every researcher has the ethical obligation to publish research results on all trials with human participants in a complete and accurate way within 12 months after the end of the trial.1,2 Nevertheless, for several reasons, not all research results are published in an accurate way in case they are released at all. This phenomenon of publication bias may not only create a false impression on the reliability of clinical research business, but it may also affect the evidence of clinical conclusions about the best treatments, which are mostly based on published data and results.

Threat analysis of IoT networks using artificial neural network intrusion detection system

The Internet of things (IoT) is still in its infancy and has attracted much interest in many industrial sectors including medical fields, logistics tracking, smart cities and automobiles. However, as a paradigm, it is susceptible to a range of significant intrusion threats. This paper presents a threat analysis of the IoT and uses an Artificial Neural Network (ANN) to combat these threats. A multi-level perceptron, a type of supervised ANN, is trained using internet packet traces, then is assessed on its ability to thwart Distributed Denial of Service (DDoS/DoS) attacks. This paper focuses on the classification of normal and threat patterns on an IoT Network. The ANN procedure is validated against a simulated IoT network. The experimental results demonstrate 99.4% accuracy and can successfully detect various DDoS/DoS attacks.

Prevalence of Aspergillus fumigatus complex in waste sorting and incineration plants: an occupational threat

Aspergillus fumigatus is one of the major ubiquitous saprophytic fungi and it is considered one of the fungal species with higher clinical relevance. This study aimed at characterising the prevalence of A. fumigatus complex in one waste-sorting plant and also in one incineration plant. Conventional and molecular methodologies were applied in order to detect its presence. Aspergillus fumigatus complex was the second most frequently found in the air from the waste-sorting plant (16.0%) and from the incineration plant (18.0%). Regarding surfaces, it ranked the third species most frequently found in the waste-sorting plant (13.8%) and the second in the incineration plant (22.3%). In the waste-sorting plant, it was possible to amplify by qPCR DNA from the A. fumigatus complex in all culture-positive sampling sites plus one other sampling site that was negative by culture analysis. Considering the observed fungal load, it is recommended to apply preventive and protective measures in order to avoid or minimise worker's exposure.