5,944 research outputs found

    The Fallacy of Systemic Racism in the American Criminal Justice System

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    Critics of the criminal justice system have repeatedly charged it with systemic racism. It is a tenet of the “war” on the “War on Drugs,” it is a justification used by the so-called “progressive prosecutors” to reject the “Broken Windows” theory of law enforcement, and it is an article of faith of the “Defund the Police!” movement. Even President Joe Biden and his chief lieutenants leveled the same allegation early in this administration. Although the President has eschewed the belief that Americans are a racist people, others have not, proclaiming that virtually anyone who is white is a racist. Yet, few people have defined what they mean by that term. This Article examines what it could mean and tests the truth of the systemic racism claim under each possible definition. None stands up to scrutiny. One argument is that the American citizens who run our many institutions are motivated by racial animus. But the evidence is that racial animus is no longer tolerated in society, and what is more, the criminal justice system strives to identify it when it does occur and to remedy it. Another argument says that the overtly racist beliefs and practices of the past have created lingering racist effects, but this argument cherry-picks historical facts (when it does not ignore them altogether) and fails to grapple with the country’s historic and ongoing efforts to eliminate racial discrimination. It also assumes a causal relationship between past discrimination and present disparities that is unsupported and often contradicted by the evidence. Yet another argument relies psychological research to claim that white Americans are animated by a subconscious racial animus. That research, however, has been debunked. Still another argument says that the criminal justice system is systemically racist because it has disparate effects across racial groups, but this argument looks only at the offenders’ side of the criminal justice system and fails to consider the effect of the criminal justice system on victims. Proponents of the systemic racism theory often proffer “solutions” to it. This Article examines those too and finds that many would, in fact, harm the very people they aim to help. In the context of the “War on Drugs,” where so much of the rhetoric is focused, the authors examine these arguments and solutions. The bottom line is this: the claim of systemic racism in the criminal justice system is unjustified

    The Epidemiology and Management of Kawasaki Disease in Australia

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    Kawasaki disease (KD) is a syndrome of systemic inflammation with the potential to cause life-threatening aneurysms of the coronary arteries. I sought to contribute to our understanding of this important condition, particularly with regard to Australian children. By determining the hospitalisation rate and IVIG-treatment rate I estimated the incidence of KD to be about 14 per 100,000 children under the age of 5 between 2007 and 2015. I also showed that the hospitalisation rate nationally had increased on average 3.5% annually between 1993 and 2018, with significant changes in the age distribution over that period. In collaboration with the Paediatric Active Enhanced Disease Surveillance (PAEDS) network, I undertook a large multicentre prospective surveillance study of KD in Australia. My analysis of that cohort confirmed several of the findings from the survey, such as the preference of Australian clinicians for low-dose aspirin from the time of diagnosis, and the considerable variability around how IVIG resistance is diagnosed and managed. Importantly, I observed that a significant subset of children diagnosed with, and treated for, KD do not meet the diagnostic criteria outlined in the 2017 statement by the American Heart Association. This work has contributed significantly to the understanding of KD’s epidemiology, management, and outcomes in Australia. I have shown that the incidence of the condition is increasing, and the clinical picture is changing. I identified important areas of practice variation and highlighted the need for international collaboration around agreed definitions (such as for IVIG resistance). Finally, I have played a central role in establishing an important resource for future resource: prospective surveillance of KD in Australia continues, with well over 700 cases recruited so far. It is hoped that this work will be of benefit to the researchers, clinicians, patients, and families affected by KD now, and into the future

    Safe passage for attachment systems:Can attachment security at international schools be measured, and is it at risk?

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    Relocations challenge attachment networks. Regardless of whether a person moves or is moved away from, relocation produces separation and loss. When such losses are repeatedly experienced without being adequately processed, a defensive shutting down of the attachment system could result, particularly when such experiences occur during or across the developmental years. At schools with substantial turnover, this possibility could be shaping youth in ways that compromise attachment security and young people’s willingness or ability to develop and maintain deep long-term relationships. Given the well-documented associations between attachment security, social support, and long-term physical and mental health, the hypothesis that mobility could erode attachment and relational health warrants exploration. International schools are logical settings to test such a hypothesis, given their frequently high turnover without confounding factors (e.g. war trauma or refugee experiences). In addition, repeated experiences of separation and loss in international school settings would seem likely to create mental associations for the young people involved regarding how they and others tend to respond to such situations in such settings, raising the possibility that people at such schools, or even the school itself, could collectively be represented as an attachment figure. Questions like these have received scant attention in the literature. They warrant consideration because of their potential to shape young people’s most general convictions regarding attachment, which could, in turn, have implications for young people’s ability to experience meaning in their lives

    The organisation and function of the gut microbiota in cystic fibrosis

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    People with cystic fibrosis (pwCF) suffer from a range of gastrointestinal manifestations of disease, which has increased in prevalence as respiratory outcomes improve and life expectancy raises. This translates to the occurrence of daily GI symptoms for many pwCF, which is a top research priority to alleviate. The gut microbiota is altered in CF and has been shown to associate with intestinal abnormalities, therefore offers a potential avenue of therapeutic intervention by its modulation. This thesis investigated relationships between the microbiota and associated functions, intestinal outcomes, and cystic fibrosis transmembrane regulator (CFTR) modulator usage. Initially, relationships between altered intestinal function and physiology in CF were revealed with microbiota, by combining 16S rRNA gene sequencing data with magnetic resonance imaging (MRI) results and clinical metadata across pwCF and healthy controls. Significant differences in diversity and composition were observed between groups, which further associated with clinical factors and markers of intestinal function. To understand how microbiota function might be compromised in CF, a sensitive method to profile and quantify faecal short-chain fatty acids (SCFAs) using gas chromatography-mass spectrometry (GC-MS) was validated. This was subsequently used to demonstrate that overall SCFA compositional differences persist between healthy controls pwCF receiving Tezacaftor/Ivacaftor CFTR modulator therapy, further extending to microbiota compositional differences, which were also not significantly altered by treatment. Finally, microbiota composition and function were assessed across pwCF receiving more efficacious Elexacaftor/Tezacaftor/Ivacaftor (ETI) therapy. Subtle differences were observed following extended ETI administration, yet microbiota and SCFA compositions remained significantly different from controls. Interestingly, there were no differences across the most abundant SCFAs, indicating possible functional redundancy in the CF microbiota. Overall, the results obtained in this thesis advocate for further investigation of microbiota function through more sophisticated metagenomic and untargeted metabolomic approaches, to unravel the complex relationships between the microbiota, gastrointestinal (GI) manifestations, and patient symptoms in CF

    Effects of municipal smoke-free ordinances on secondhand smoke exposure in the Republic of Korea

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    ObjectiveTo reduce premature deaths due to secondhand smoke (SHS) exposure among non-smokers, the Republic of Korea (ROK) adopted changes to the National Health Promotion Act, which allowed local governments to enact municipal ordinances to strengthen their authority to designate smoke-free areas and levy penalty fines. In this study, we examined national trends in SHS exposure after the introduction of these municipal ordinances at the city level in 2010.MethodsWe used interrupted time series analysis to assess whether the trends of SHS exposure in the workplace and at home, and the primary cigarette smoking rate changed following the policy adjustment in the national legislation in ROK. Population-standardized data for selected variables were retrieved from a nationally representative survey dataset and used to study the policy action’s effectiveness.ResultsFollowing the change in the legislation, SHS exposure in the workplace reversed course from an increasing (18% per year) trend prior to the introduction of these smoke-free ordinances to a decreasing (−10% per year) trend after adoption and enforcement of these laws (ÎČ2 = 0.18, p-value = 0.07; ÎČ3 = −0.10, p-value = 0.02). SHS exposure at home (ÎČ2 = 0.10, p-value = 0.09; ÎČ3 = −0.03, p-value = 0.14) and the primary cigarette smoking rate (ÎČ2 = 0.03, p-value = 0.10; ÎČ3 = 0.008, p-value = 0.15) showed no significant changes in the sampled period. Although analyses stratified by sex showed that the allowance of municipal ordinances resulted in reduced SHS exposure in the workplace for both males and females, they did not affect the primary cigarette smoking rate as much, especially among females.ConclusionStrengthening the role of local governments by giving them the authority to enact and enforce penalties on SHS exposure violation helped ROK to reduce SHS exposure in the workplace. However, smoking behaviors and related activities seemed to shift to less restrictive areas such as on the streets and in apartment hallways, negating some of the effects due to these ordinances. Future studies should investigate how smoke-free policies beyond public places can further reduce the SHS exposure in ROK

    On Age-of-Information Aware Resource Allocation for Industrial Control-Communication-Codesign

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    Unter dem Überbegriff Industrie 4.0 wird in der industriellen Fertigung die zunehmende Digitalisierung und Vernetzung von industriellen Maschinen und Prozessen zusammengefasst. Die drahtlose, hoch-zuverlĂ€ssige, niedrig-latente Kommunikation (engl. ultra-reliable low-latency communication, URLLC) – als Bestandteil von 5G gewĂ€hrleistet höchste DienstgĂŒten, die mit industriellen drahtgebundenen Technologien vergleichbar sind und wird deshalb als Wegbereiter von Industrie 4.0 gesehen. Entgegen diesem Trend haben eine Reihe von Arbeiten im Forschungsbereich der vernetzten Regelungssysteme (engl. networked control systems, NCS) gezeigt, dass die hohen DienstgĂŒten von URLLC nicht notwendigerweise erforderlich sind, um eine hohe RegelgĂŒte zu erzielen. Das Co-Design von Kommunikation und Regelung ermöglicht eine gemeinsame Optimierung von RegelgĂŒte und Netzwerkparametern durch die Aufweichung der Grenze zwischen Netzwerk- und Applikationsschicht. Durch diese VerschrĂ€nkung wird jedoch eine fundamentale (gemeinsame) Neuentwicklung von Regelungssystemen und Kommunikationsnetzen nötig, was ein Hindernis fĂŒr die Verbreitung dieses Ansatzes darstellt. Stattdessen bedient sich diese Dissertation einem Co-Design-Ansatz, der beide DomĂ€nen weiterhin eindeutig voneinander abgrenzt, aber das Informationsalter (engl. age of information, AoI) als bedeutenden Schnittstellenparameter ausnutzt. Diese Dissertation trĂ€gt dazu bei, die EchtzeitanwendungszuverlĂ€ssigkeit als Folge der Überschreitung eines vorgegebenen Informationsalterschwellenwerts zu quantifizieren und fokussiert sich dabei auf den Paketverlust als Ursache. Anhand der Beispielanwendung eines fahrerlosen Transportsystems wird gezeigt, dass die zeitlich negative Korrelation von Paketfehlern, die in heutigen Systemen keine Rolle spielt, fĂŒr Echtzeitanwendungen Ă€ußerst vorteilhaft ist. Mit der Annahme von schnellem Schwund als dominanter Fehlerursache auf der Luftschnittstelle werden durch zeitdiskrete Markovmodelle, die fĂŒr die zwei Netzwerkarchitekturen Single-Hop und Dual-Hop prĂ€sentiert werden, Kommunikationsfehlerfolgen auf einen Applikationsfehler abgebildet. Diese Modellierung ermöglicht die analytische Ableitung von anwendungsbezogenen ZuverlĂ€ssigkeitsmetriken wie die durschnittliche Dauer bis zu einem Fehler (engl. mean time to failure). FĂŒr Single-Hop-Netze wird das neuartige Ressourcenallokationsschema State-Aware Resource Allocation (SARA) entwickelt, das auf dem Informationsalter beruht und die AnwendungszuverlĂ€ssigkeit im Vergleich zu statischer Multi-KonnektivitĂ€t um GrĂ¶ĂŸenordnungen erhöht, wĂ€hrend der Ressourcenverbrauch im Bereich von konventioneller EinzelkonnektivitĂ€t bleibt. Diese ZuverlĂ€ssigkeit kann auch innerhalb eines Systems von Regelanwendungen, in welchem mehrere Agenten um eine begrenzte Anzahl Ressourcen konkurrieren, statistisch garantiert werden, wenn die Anzahl der verfĂŒgbaren Ressourcen pro Agent um ca. 10 % erhöht werden. FĂŒr das Dual-Hop Szenario wird darĂŒberhinaus ein Optimierungsverfahren vorgestellt, das eine benutzerdefinierte Kostenfunktion minimiert, die niedrige AnwendungszuverlĂ€ssigkeit, hohes Informationsalter und hohen durchschnittlichen Ressourcenverbrauch bestraft und so das benutzerdefinierte optimale SARA-Schema ableitet. Diese Optimierung kann offline durchgefĂŒhrt und als Look-Up-Table in der unteren Medienzugriffsschicht zukĂŒnftiger industrieller Drahtlosnetze implementiert werden.:1. Introduction 1 1.1. The Need for an Industrial Solution . . . . . . . . . . . . . . . . . . . 3 1.2. Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2. Related Work 7 2.1. Communications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2. Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3. Codesign . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.3.1. The Need for Abstraction – Age of Information . . . . . . . . 11 2.4. Dependability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3. Deriving Proper Communications Requirements 17 3.1. Fundamentals of Control Theory . . . . . . . . . . . . . . . . . . . . 18 3.1.1. Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.1.2. Performance Requirements . . . . . . . . . . . . . . . . . . . 21 3.1.3. Packet Losses and Delay . . . . . . . . . . . . . . . . . . . . . 22 3.2. Joint Design of Control Loop with Packet Losses . . . . . . . . . . . . 23 3.2.1. Method 1: Reduced Sampling . . . . . . . . . . . . . . . . . . 23 3.2.2. Method 2: Markov Jump Linear System . . . . . . . . . . . . . 25 3.2.3. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.3. Focus Application: The AGV Use Case . . . . . . . . . . . . . . . . . . 31 3.3.1. Control Loop Model . . . . . . . . . . . . . . . . . . . . . . . 31 3.3.2. Control Performance Requirements . . . . . . . . . . . . . . . 33 3.3.3. Joint Modeling: Applying Reduced Sampling . . . . . . . . . . 34 3.3.4. Joint Modeling: Applying MJLS . . . . . . . . . . . . . . . . . 34 3.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4. Modeling Control-Communication Failures 43 4.1. Communication Assumptions . . . . . . . . . . . . . . . . . . . . . . 43 4.1.1. Small-Scale Fading as a Cause of Failure . . . . . . . . . . . . 44 4.1.2. Connectivity Models . . . . . . . . . . . . . . . . . . . . . . . 46 4.2. Failure Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.2.1. Single-hop network . . . . . . . . . . . . . . . . . . . . . . . . 49 4.2.2. Dual-hop network . . . . . . . . . . . . . . . . . . . . . . . . 51 4.3. Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 4.3.1. Mean Time to Failure . . . . . . . . . . . . . . . . . . . . . . . 54 4.3.2. Packet Loss Ratio . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.3.3. Average Number of Assigned Channels . . . . . . . . . . . . . 57 4.3.4. Age of Information . . . . . . . . . . . . . . . . . . . . . . . . 57 4.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 5. Single Hop – Single Agent 61 5.1. State-Aware Resource Allocation . . . . . . . . . . . . . . . . . . . . 61 5.2. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 5.3. Erroneous Acknowledgments . . . . . . . . . . . . . . . . . . . . . . 67 5.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 6. Single Hop – Multiple Agents 71 6.1. Failure Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 6.1.1. Admission Control . . . . . . . . . . . . . . . . . . . . . . . . 72 6.1.2. Transition Probabilities . . . . . . . . . . . . . . . . . . . . . . 73 6.1.3. Computational Complexity . . . . . . . . . . . . . . . . . . . 74 6.1.4. Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . 75 6.2. Illustration Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 6.3. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 6.3.1. Verification through System-Level Simulation . . . . . . . . . 78 6.3.2. Applicability on the System Level . . . . . . . . . . . . . . . . 79 6.3.3. Comparison of Admission Control Schemes . . . . . . . . . . 80 6.3.4. Impact of the Packet Loss Tolerance . . . . . . . . . . . . . . . 82 6.3.5. Impact of the Number of Agents . . . . . . . . . . . . . . . . . 84 6.3.6. Age of Information . . . . . . . . . . . . . . . . . . . . . . . . 84 6.3.7. Channel Saturation Ratio . . . . . . . . . . . . . . . . . . . . 86 6.3.8. Enforcing Full Channel Saturation . . . . . . . . . . . . . . . 86 6.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 7. Dual Hop – Single Agent 91 7.1. State-Aware Resource Allocation . . . . . . . . . . . . . . . . . . . . 91 7.2. Optimization Targets . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 7.3. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 7.3.1. Extensive Simulation . . . . . . . . . . . . . . . . . . . . . . . 96 7.3.2. Non-Integer-Constrained Optimization . . . . . . . . . . . . . 98 7.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 8. Conclusions and Outlook 105 8.1. Key Results and Conclusions . . . . . . . . . . . . . . . . . . . . . . . 105 8.2. Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 A. DC Motor Model 111 Bibliography 113 Publications of the Author 127 List of Figures 129 List of Tables 131 List of Operators and Constants 133 List of Symbols 135 List of Acronyms 137 Curriculum Vitae 139In industrial manufacturing, Industry 4.0 refers to the ongoing convergence of the real and virtual worlds, enabled through intelligently interconnecting industrial machines and processes through information and communications technology. Ultrareliable low-latency communication (URLLC) is widely regarded as the enabling technology for Industry 4.0 due to its ability to fulfill highest quality-of-service (QoS) comparable to those of industrial wireline connections. In contrast to this trend, a range of works in the research domain of networked control systems have shown that URLLC’s supreme QoS is not necessarily required to achieve high quality-ofcontrol; the co-design of control and communication enables to jointly optimize and balance both quality-of-control parameters and network parameters through blurring the boundary between application and network layer. However, through the tight interlacing, this approach requires a fundamental (joint) redesign of both control systems and communication networks and may therefore not lead to short-term widespread adoption. Therefore, this thesis instead embraces a novel co-design approach which keeps both domains distinct but leverages the combination of control and communications by yet exploiting the age of information (AoI) as a valuable interface metric. This thesis contributes to quantifying application dependability as a consequence of exceeding a given peak AoI with the particular focus on packet losses. The beneficial influence of negative temporal packet loss correlation on control performance is demonstrated by means of the automated guided vehicle use case. Assuming small-scale fading as the dominant cause of communication failure, a series of communication failures are mapped to an application failure through discrete-time Markov models for single-hop (e.g, only uplink or downlink) and dual-hop (e.g., subsequent uplink and downlink) architectures. This enables the derivation of application-related dependability metrics such as the mean time to failure in closed form. For single-hop networks, an AoI-aware resource allocation strategy termed state-aware resource allocation (SARA) is proposed that increases the application reliability by orders of magnitude compared to static multi-connectivity while keeping the resource consumption in the range of best-effort single-connectivity. This dependability can also be statistically guaranteed on a system level – where multiple agents compete for a limited number of resources – if the provided amount of resources per agent is increased by approximately 10 %. For the dual-hop scenario, an AoI-aware resource allocation optimization is developed that minimizes a user-defined penalty function that punishes low application reliability, high AoI, and high average resource consumption. This optimization may be carried out offline and each resulting optimal SARA scheme may be implemented as a look-up table in the lower medium access control layer of future wireless industrial networks.:1. Introduction 1 1.1. The Need for an Industrial Solution . . . . . . . . . . . . . . . . . . . 3 1.2. Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2. Related Work 7 2.1. Communications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2. Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3. Codesign . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.3.1. The Need for Abstraction – Age of Information . . . . . . . . 11 2.4. Dependability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3. Deriving Proper Communications Requirements 17 3.1. Fundamentals of Control Theory . . . . . . . . . . . . . . . . . . . . 18 3.1.1. Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.1.2. Performance Requirements . . . . . . . . . . . . . . . . . . . 21 3.1.3. Packet Losses and Delay . . . . . . . . . . . . . . . . . . . . . 22 3.2. Joint Design of Control Loop with Packet Losses . . . . . . . . . . . . 23 3.2.1. Method 1: Reduced Sampling . . . . . . . . . . . . . . . . . . 23 3.2.2. Method 2: Markov Jump Linear System . . . . . . . . . . . . . 25 3.2.3. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.3. Focus Application: The AGV Use Case . . . . . . . . . . . . . . . . . . 31 3.3.1. Control Loop Model . . . . . . . . . . . . . . . . . . . . . . . 31 3.3.2. Control Performance Requirements . . . . . . . . . . . . . . . 33 3.3.3. Joint Modeling: Applying Reduced Sampling . . . . . . . . . . 34 3.3.4. Joint Modeling: Applying MJLS . . . . . . . . . . . . . . . . . 34 3.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4. Modeling Control-Communication Failures 43 4.1. Communication Assumptions . . . . . . . . . . . . . . . . . . . . . . 43 4.1.1. Small-Scale Fading as a Cause of Failure . . . . . . . . . . . . 44 4.1.2. Connectivity Models . . . . . . . . . . . . . . . . . . . . . . . 46 4.2. Failure Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.2.1. Single-hop network . . . . . . . . . . . . . . . . . . . . . . . . 49 4.2.2. Dual-hop network . . . . . . . . . . . . . . . . . . . . . . . . 51 4.3. Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 4.3.1. Mean Time to Failure . . . . . . . . . . . . . . . . . . . . . . . 54 4.3.2. Packet Loss Ratio . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.3.3. Average Number of Assigned Channels . . . . . . . . . . . . . 57 4.3.4. Age of Information . . . . . . . . . . . . . . . . . . . . . . . . 57 4.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 5. Single Hop – Single Agent 61 5.1. State-Aware Resource Allocation . . . . . . . . . . . . . . . . . . . . 61 5.2. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 5.3. Erroneous Acknowledgments . . . . . . . . . . . . . . . . . . . . . . 67 5.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 6. Single Hop – Multiple Agents 71 6.1. Failure Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 6.1.1. Admission Control . . . . . . . . . . . . . . . . . . . . . . . . 72 6.1.2. Transition Probabilities . . . . . . . . . . . . . . . . . . . . . . 73 6.1.3. Computational Complexity . . . . . . . . . . . . . . . . . . . 74 6.1.4. Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . 75 6.2. Illustration Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 6.3. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 6.3.1. Verification through System-Level Simulation . . . . . . . . . 78 6.3.2. Applicability on the System Level . . . . . . . . . . . . . . . . 79 6.3.3. Comparison of Admission Control Schemes . . . . . . . . . . 80 6.3.4. Impact of the Packet Loss Tolerance . . . . . . . . . . . . . . . 82 6.3.5. Impact of the Number of Agents . . . . . . . . . . . . . . . . . 84 6.3.6. Age of Information . . . . . . . . . . . . . . . . . . . . . . . . 84 6.3.7. Channel Saturation Ratio . . . . . . . . . . . . . . . . . . . . 86 6.3.8. Enforcing Full Channel Saturation . . . . . . . . . . . . . . . 86 6.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 7. Dual Hop – Single Agent 91 7.1. State-Aware Resource Allocation . . . . . . . . . . . . . . . . . . . . 91 7.2. Optimization Targets . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 7.3. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 7.3.1. Extensive Simulation . . . . . . . . . . . . . . . . . . . . . . . 96 7.3.2. Non-Integer-Constrained Optimization . . . . . . . . . . . . . 98 7.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 8. Conclusions and Outlook 105 8.1. Key Results and Conclusions . . . . . . . . . . . . . . . . . . . . . . . 105 8.2. Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 A. DC Motor Model 111 Bibliography 113 Publications of the Author 127 List of Figures 129 List of Tables 131 List of Operators and Constants 133 List of Symbols 135 List of Acronyms 137 Curriculum Vitae 13
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