631 research outputs found

    Characterizing the performance of low impact development under changes in climate and urbanization

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    Over the past decades, climate change and urbanization have altered the regional hydro-environments, causing a series of stormwater management problems including urban flood and non-point pollution. Low impact development (LID) has been identified as a sustainable strategy for stormwater management. However, given the complex impacts of climate change and urbanization on hydro-environments, the performance of LID strategy under future changes remains largely unexplored. Accordingly, this research characterized the LID performance under changes in climate and urbanization. To provide an additional reference to sustainable stormwater management, the following specific topics were addressed: (1) Through hydraulic and water quality modeling, the LID performances of flood mitigation and pollution removal were systematically evaluated at the city scale. (2) Through uncertainty analysis, the impact of model parameter uncertainty on the LID performance was taken into account. (3) Through sensitivity analysis, the impact of LID technical parameters (e.g., surface features, soil textures) on the LID performance was quantified. (4) Through scenario analysis, the LID performances under different hydrological patterns were compared. (5) Through spatial analysis, the distribution of LID performance on different land-cover types was determined. (6) Through adopting general circulation model (GCM) projections, the LID performance under future climate scenarios with different representative concentration pathways (RCPs) was investigated. (7) Through adopting integrated assessment model (IAM) projections, the LID performance under future urbanization scenarios with different shared socioeconomic pathways (SSPs) was explored. (8) By coupling climate and urbanization projections with land-cover distribution, the spatiotemporal trends of LID performance in the future were characterized.:Table of Contents List of Abbreviations VII List of Peer-Reviewed Publications on the Ph.D. Topic IX List of Co-authored Peer-Reviewed Publications on the Ph.D. Topic X 1 General Introduction 1 1.1 Background 1 1.2 Objectives 3 1.3 Innovation and Contribution to the Knowledge 3 1.4 Outline of the Dissertation 4 1.5 References 5 2 Literature Review 9 2.1 Hydraulic and Water Quality Modeling 9 2.1.1 Hydraulic Model 9 2.1.2 Water Quality Model 10 2.2 Low Impact Development (LID) 10 2.2.1 LID Practice 10 2.2.2 LID Performance 11 2.3 Performance Evaluation 13 2.3.1 Scenario Analysis 13 2.3.2 Spatial Analysis 13 2.3.3 Uncertainty Analysis 14 2.3.4 Sensitivity Analysis 14 2.4 Future Changes in Climate and Urbanization 15 2.4.1 Climate Change 15 2.4.2 Future Urbanization 16 2.5 References 17 3 Impact of Technical Factors on LID Performance 27 3.1 Introduction 28 3.2 Methods 30 3.2.1 Study Area 30 3.2.2 Model Description 31 3.2.2.1 Model Theory 31 3.2.2.2 Model Construction 31 3.2.2.3 Model Calibration and Validation 32 3.2.2.4 Model Uncertainty Analysis by GLUE Method 34 3.2.3 Hydrological Pattern Design 35 3.2.4 LID Strategy Design 35 3.2.4.1 Implementation of LID Practices 35 3.2.4.2 Sensitivity Analysis by Sobol’s Method 36 3.2.5 Correlation Analysis Using a Self-Organizing Map 37 3.2.6 Description of the RDS Load Components 37 3.3 Results 38 3.3.1 RDS Migration and Distribution in Baseline Strategy 38 3.3.1.1 RDS Migration under Hydrological Scenarios 38 3.3.1.2 RDS Distribution on Land-Cover Types 39 3.3.2 RDS Removal in LID Strategies 40 3.3.2.1 RDS Removal by LID Strategies 40 3.3.2.2 Spatial Distribution of the RDS Removal 42 3.3.2.3 LID Parameter Sensitivity Analysis Result 43 3.4 Discussion 45 3.4.1 Factors Influencing RDS Migration in the Baseline Strategy 45 3.4.2 RDS Removal Performance by LID Strategy 46 3.5 Conclusions 47 3.6 References 47 4 Impact of Hydro-Environmental Factors on LID Performance 53 4.1 Introduction 54 4.2 Methods 56 4.2.1 Study Area 56 4.2.2 Modeling Approach 56 4.2.2.1 Model Theory 56 4.2.2.2 Model Construction 56 4.2.2.3 Model Calibration and Validation 57 4.2.2.4 Model Uncertainty Analysis 57 4.2.3 LID Performance Analysis 58 4.2.3.1 LID Practice Implementation 58 4.2.3.2 LID Performance Evaluation 58 4.2.4 Hydrological Pattern Analysis 59 4.2.4.1 Scenario of ADP Length 59 4.2.4.2 Scenario of Rainfall Magnitude 59 4.2.4.3 Scenario of Long-Term pre-Simulation 60 4.2.5 Sensitivity Analysis of Hydrological Scenarios 60 4.3 Results 61 4.3.1 LID Performance under Different ADP Lengths 61 4.3.2 LID Performance under Different Rainfall Magnitudes 62 4.3.3 Spatial Distribution of LID Performance 63 4.3.4 Sensitivities of LID Performance to ADP Length and Rainfall Magnitude 66 4.4 Discussion 68 4.4.1 Impact of ADP Length and Rainfall Magnitude on LID Performance 68 4.4.2 Spatial Heterogeneity of LID Performance 68 4.4.3 Research Implications 69 4.5 Conclusions 70 4.6 References 71 5 Impact of Future Climate Patterns on LID Performance 77 5.1 Introduction 78 5.2 Methods 80 5.2.1 Study Area 80 5.2.2 Hydraulic and Water Quality Model 80 5.2.2.1 Model Development 80 5.2.2.2 Model Calibration and Validation 81 5.2.3 Climate Change Scenario Analysis 81 5.2.3.1 GCM Evaluation 81 5.2.3.2 Greenhouse Gas (GHG) Concentration Scenario 82 5.2.3.3 GCM Downscaling 83 5.2.4 LID Performance Analysis 83 5.2.4.1 Implementation of LID Practices 83 5.2.4.2 Evaluation of LID Performance 84 5.2.4.3 Sensitivity Analysis on LID Performance 86 5.3 Results 86 5.3.1 Hydrological Characteristics under Future Climate Scenarios 86 5.3.2 LID Performance under Future Climate Scenarios 87 5.3.2.1 LID Short-Term Performance 87 5.3.2.2 LID Long-Term Performance 90 5.3.3 Impact of ADP Length and Rainfall Magnitude on LID Performance 92 5.3.3.1 LID Performance Uncertainty 92 5.3.3.2 Spatial Distribution of LID Performance 93 5.3.3.3 Sensitivity of LID Performance to Climate Change 95 5.4 Discussion 97 5.4.1 LID Performance in Short-Term Extremes and Long-Term Events 97 5.4.2 Impact of Climate Change on LID Performance 97 5.4.3 Research Implications 99 5.5 Conclusions 100 5.6 References 100 6 Impact of Climate and Urbanization Changes on LID Perfor-mance 109 6.1 Introduction 110 6.2 Methods 112 6.2.1 Study Area 112 6.2.2 Modeling Approach 112 6.2.2.1 Model Development 112 6.2.2.2 Model Calibration and Validation 113 6.2.3 Future Scenario Derivation 113 6.2.3.1 Climate Change Scenario 113 6.2.3.2 Urbanization Scenario 115 6.2.4 Flood Exposure Assessment 115 6.2.5 Implementation and Evaluation of LID Strategy 117 6.2.5.1 Implementation Scheme of LID Strategy 117 6.2.5.2 Performance Evaluation of LID Strategy 117 6.3 Results 118 6.3.1 Flood Exposure in Baseline and Future Scenarios 118 6.3.1.1 Hydrological Change in Future Climate Scenarios 118 6.3.1.2 Catchment Change in Future Urbanization Scenarios 118 6.3.1.3 Population and GDP Exposures to Flood in Future 121 6.3.2 Flood Exposure with Consideration of LID Strategy 123 6.3.2.1 Runoff Mitigation Performance of LID Strategy 123 6.3.2.2 Peak Mitigation Performance of LID Strategy 124 6.3.2.3 Population and GDP Exposures to Flood under LID Strategy 125 6.4 Discussion 126 6.4.1 Climate Change and Urbanization Exacerbated Flood Exposure Risk 126 6.4.2 LID Strategy Mitigated Flood Exposure Risk 126 6.5 Conclusions 127 6.6 References 127 7 Discussion and General Conclusions 133 7.1 Stormwater Management Performance of LID Strategy 133 7.2 Impact of Influencing Factors on LID Performance 134 7.3 LID Performance under Future Changes 135 7.4 Research Implications 136 7.5 References 137 8 Outlook of Future Research 139 8.1 Optimization of LID Performance 139 8.2 Cross-regional Study on Future Changes 139 8.3 Macro-scale Flood Risk Management 140 8.4 References 141 9 Appendices 143 9.1 Appendix for Chapter 3 143 9.1.1 The Determination of the GLUE Criteria 143 9.1.2 Model Uncertainty Analysis 143 9.1.3 The LID Installation Location 144 9.1.4 Figures 145 9.1.5 Tables 147 9.2 Appendix for Chapter 4 153 9.2.1 Scenario of Long-term pre-Simulation 153 9.2.2 Figures 153 9.2.3 Tables 158 9.3 Appendix for Chapter 5 164 9.3.1 Tables 164 9.4 Appendix for Chapter 6 169 9.4.1 Figures 169 9.4.2 Tables 170 9.5 Data Source 177 9.6 References 17

    Revisiting Language Support for Generic Programming: When Genericity Is a Core Design Goal

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    ContextGeneric programming, as defined by Stepanov, is a methodology for writing efficient and reusable algorithms by considering only the required properties of their underlying data types and operations. Generic programming has proven to be an effective means of constructing libraries of reusable software components in languages that support it. Generics-related language design choices play a major role in how conducive generic programming is in practice.InquirySeveral mainstream programming languages (e.g. Java and C++) were first created without generics; features to support generic programming were added later, gradually. Much of the existing literature on supporting generic programming focuses thus on retrofitting generic programming into existing languages and identifying related implementation challenges. Is the programming experience significantly better, or different when programming with a language designed for generic programming without limitations from prior language design choices?ApproachWe examine Magnolia, a language designed to embody generic programming. Magnolia is representative of an approach to language design rooted in algebraic specifications. We repeat a well-known experiment, where we put Magnolia’s generic programming facilities under scrutiny by implementing a subset of the Boost Graph Library, and reflect on our development experience.KnowledgeWe discover that the idioms identified as key features for supporting Stepanov-style generic programming in the previous studies and work on the topic do not tell a full story. We clarify which of them are more of a means to an end, rather than fundamental features for supporting generic programming. Based on the development experience with Magnolia, we identify variadics as an additional key feature for generic programming and point out limitations and challenges of genericity by property.GroundingOur work uses a well-known framework for evaluating the generic programming facilities of a language from the literature to evaluate the algebraic approach through Magnolia, and we draw comparisons with well-known programming languages.ImportanceThis work gives a fresh perspective on generic programming, and clarifies what are fundamental language properties and their trade-offs when considering supporting Stepanov-style generic programming. The understanding of how to set the ground for generic programming will inform future language design.</p

    Cybersecurity: Past, Present and Future

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    The digital transformation has created a new digital space known as cyberspace. This new cyberspace has improved the workings of businesses, organizations, governments, society as a whole, and day to day life of an individual. With these improvements come new challenges, and one of the main challenges is security. The security of the new cyberspace is called cybersecurity. Cyberspace has created new technologies and environments such as cloud computing, smart devices, IoTs, and several others. To keep pace with these advancements in cyber technologies there is a need to expand research and develop new cybersecurity methods and tools to secure these domains and environments. This book is an effort to introduce the reader to the field of cybersecurity, highlight current issues and challenges, and provide future directions to mitigate or resolve them. The main specializations of cybersecurity covered in this book are software security, hardware security, the evolution of malware, biometrics, cyber intelligence, and cyber forensics. We must learn from the past, evolve our present and improve the future. Based on this objective, the book covers the past, present, and future of these main specializations of cybersecurity. The book also examines the upcoming areas of research in cyber intelligence, such as hybrid augmented and explainable artificial intelligence (AI). Human and AI collaboration can significantly increase the performance of a cybersecurity system. Interpreting and explaining machine learning models, i.e., explainable AI is an emerging field of study and has a lot of potentials to improve the role of AI in cybersecurity.Comment: Author's copy of the book published under ISBN: 978-620-4-74421-

    Structural optimization in steel structures, algorithms and applications

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    L'abstract è presente nell'allegato / the abstract is in the attachmen

    Scaling energy management in buildings with artificial intelligence

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    L'abstract è presente nell'allegato / the abstract is in the attachmen

    Development of an Integrated thermal hydrolysis process - Anaerobic Digestion (THPAD) Model

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    Historically, anaerobic digestion is one of the most common processes used to treat sludge generated from wastewater treatment plant (WWTP) processes. However, with the exponential increase in populations, which implies an increase in WWTP loads, the amount of waste generated poses an imminent problem to the handling capacity of current anaerobic digesters. Subsequently, there has been a lot of research into various physical and chemical processes that would allow for a more efficient sludge handling mechanism. Studies have reported various advantages associated with digesting sludge at higher temperatures known as thermophilic temperatures. These advantages include increased sludge handling capacity, a higher degree of sludge biodegradability and subsequently increased methane production and better sludge dewatering characteristics implying cheaper sludge transportation costs just to mention a few. However, despite the advantages associated with thermal treatment, this technology has not yet been proven in a South African context. This project involved the development of an integrated thermal hydrolysis process (THP) and anaerobic digestion (AD) model capable of simulating these processes at elevated temperatures. A comparative desktop case study of the existing AD facility at the Cape Flats wastewater treatment works (CFWWTW) in Western Cape, South Africa was investigated following the City of Cape Town's (CCT) initiative to retrofit a THP unit to the anaerobic digesters to help deal with the increase in sludge handling capacity. A comparison was therefore carried out, investigating the base case scenario of maintaining the existing conventional mesophilic anaerobic digesters (MAD) and retrofitting a THP unit to the conventional anaerobic digesters (THPAD). A steady-state THP and AD model was developed and used in conjunction with an integrated dynamic THP and modified AD (termed as the Extended-UCTSDM3P) model for simulating both the conventional MAD and THPAD processes. This allowed for a comparison of results not only between the two processes, but also the two types of models. These models were then used to simulate the treatment of a mixture of primary sludge (PS) and waste activated sludge (WAS) at a ratio of 60:40 with the WAS obtained from a Nitrification Denitrification Biological Excess Phosphorus Removal (NDBEPR) activated sludge treatment. The AD models, therefore, accounted for the increased phosphorus concentration as a result of iv polyphosphates (PP) breakdown and consequently the possible precipitation of struvite (MgNH4PO4) from the AD liquor. The results showed that the THPAD configuration allowed the digesters to process 2.3 times more sludge than with the conventional mesophilic anaerobic digesters. Furthermore, the methane production in the THPAD was conservatively calculated to be 2.5 times higher than the MAD. This implied an increased potential for use of the methane gas as an alternative source of energy in the wastewater treatment plants. Given that no laboratory experiments were carried out, the results were based on theoretical scenarios and knowledge collected from an extensive literature review. However, given the capacity, flexibility and detail the model has been developed to, different scenarios in the anaerobic digestion process can be investigated and valuable practical insight extracted. Furthermore, through calibration with accurate meaningful data from a pilot or full-scale plant, the developed model is a tool that could be used in predicting digester performance

    Microeconomic adaptation in social-ecological systems

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    Henry Bartelet explored the socio-economic resilience of dive tourism on the Great Barrier Reef and other Pacific communities that depend economically on coral reefs. He identified complex and dynamic relationships between adaptive capacity, adaptive responses, and adaption outcomes. His thesis extends and refines existing and emerging climate change adaptation theories

    Occupant-Centric Simulation-Aided Building Design Theory, Application, and Case Studies

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    This book promotes occupants as a focal point for the design process

    Private 5G and its Suitability for Industrial Networking

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    5G was and is still surrounded by many promises and buzzwords, such as the famous 1 ms, real-time, and Ultra-Reliable and Low-Latency Communications (URLLC). This was partly intended to get the attention of vertical industries to become new customers for mobile networks, which shall be deployed in their factories. With the allowance of federal agencies, companies deployed their own private 5G networks to test new use cases enabled by 5G. But what has been missing, apart from all the marketing, is the knowledge of what 5G can really do? Private 5G networks are envisioned to enable new use cases with strict latency requirements, such as robot control. This work has examined in great detail the capabilities of the current 5G Release 15 as private network, and in particular its suitability with regard to time-critical communications. For that, a testbed was designed to measure One-Way Delays (OWDs) and Round-Trip Times (RTTs) with high accuracy. The measurements were conducted in 5G Non-Standalone (NSA) and Standalone (SA) net-works and are the first published results. The evaluation revealed results that were not obvious or identified by previous work. For example, a strong impact of the packet rate on the resulting OWD and RTT was found. It was also found that typically 95% of the SA downlink end-to-end packet delays are in the range of 4 ms to 10 ms, indicating a fairly wide spread of packet delays, with the Inter-Packet Delay Variation (IPDV) between consecutive packets distributed in the millisecond range. Surprisingly, it also seems to matter for the RTT from which direction, i.e. Downlink (DL) or Uplink (UL), a round-trip communication was initiated. Another important factor plays especially the Inter-Arrival Time (IAT) of packets on the RTT distribution. These examples from the results found demonstrate the need to critically examine 5G and any successors in terms of their real-time capabilities. In addition to the end-to-end OWD and RTT, the delays caused by 4G and 5G Core processing has been investigated as well. Current state-of-the-art 4G and 5G Core implementations exhibit long-tailed delay distributions. To overcome such limitations, modern packet processing have been evaluated in terms of their respective tail-latency. The hardware-based solution was able to process packets with deterministic delay, but the software-based solutions also achieved soft real-time results. These results allow the selection of the right technology for use cases depending on their tail-latency requirements. In summary, many insights into the suitability of 5G for time-critical communications were gained from the study of the current 5G Release 15. The measurement framework, analysis methods, and results will inform the further development and refinement of private 5G campus networks for industrial use cases
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