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The ever increasing demands of Internet users caused by the introduction of new high bandwidth applications and online services as well as the growing number of users and devices connected to the Internet, bring many challenges for the operators, especially in the last mile section of the network. Next generation access architectures are expected to offer high sustainable bandwidth per user. They also need to support a much larger service areas to decrease number of current central offices and hence potentially save the network expenditures in the future. Obviously, it requires high capacity and low loss transmission and optical fiber technology is the only future proof candidates for broadband access. Although this technology has already been widely deployed in the core networks, it is hard to use the same expensive devices made for core segment to solve the last mile bottlenecks, due to the low number of users sharing the network resources (and deployment cost). Therefore, the next generation optical access (NGOA) networks need to be designed with consideration of cost efficiency in the first place.   Network reliability is also turning to be an important aspect for the NGOA networks as a consequence of long reach, high client count and new services requiring uninterrupted access. Consequently, new architectures not only need to be cost efficient but also they should fulfill the increasing reliability requirements.   Although several NGOA alternatives have been proposed in the literatures, there is not yet an agreement on a single architecture. As described earlier, network expenditure and reliability performance are the two main factors to be considered. Therefore, this thesis concentrates on finding a suitable alternative for future broadband access by evaluating the reliability performance and total cost of ownership for several NGOA candidates. In particular, in this thesis we analyze the tradeoff between the cost needed to deploy backup resources and the reliability performance improvement obtained by the provided survivability mechanism.   First, we identified the suitable NGOA candidates by comparing two main groups of optical access networks, namely passive optical networks (PONs) and active optical networks (AONs), in terms of cost, reliability performance and power consumption. The initial results have shown that wavelength division multiplexing PON (WDM PON) is the most promising alternative for the NGOA networks because of its high potential capacity, low cost and power consumption. So we continued our studies by investigating two WDM-based PON architectures regarding their cost and reliability performance. The study has also included a proposed fiber layout compatible with these two candidates aiming to minimize the required investment needed to offer protection. Our primary results confirmed that hybrid PON (HPON) is the best alternative for the NGOA networks. Therefore we further analyzed this candidate considering several variants of HPON. The most important components and sections of the HPON, which need to be protected to decrease the impact of each failure in the network have been identified. Based on these outcomes, two resilience architectures protecting the shared part of the HPON were proposed and their reliability performance parameters as well as cost of protection were evaluated. According to the results, using our proposed protection schemes a considerable improvement in reliability performance of the HPON variants can be provided at minor extra investment. We also introduced a cost efficient HPON architecture with different levels of protection for users with various reliability requirements, i.e. the protection of shared parts of the access network for all the connected users and end-to-end resilience scheme for some selected ones (e.g., business users). To gain an overall view on the cost efficiency of the proposed architecture, we evaluated the investment required for deploying these schemes considering several network upgrading paths towards a protected network. Moreover, a sensitivity analysis investigating the influence of network deployments time and the density of the users with higher availability requirements was presented.   In summary, we have shown that HPON is able to fulfill the main NGOA requirements such as high bandwidth per-user, large coverage and client count. The work carried out in the thesis has proved that HPON can also offer high reliability performance while keeping the network expenditures at an acceptable level. Moreover, low power consumption and high flexibility in resource allocation of this architecture, makes it a winning candidate for the NGOA networks. Therefore, HPON is a promising architecture to be deployed as NGOA network in the near future considering the fact that components are soon to be available in the market. / <p>QC 20130530</p> / FP7 EU project, Optical Access Seamless Evolution(OASE)

The next generation wireless system (NGWS) has been conceived as a ubiquitous wireless environment. It integrates existing heterogeneous access networks, as well as future networks, and will offer high speed data, real-time applications (e.g. Voice over IP, videoconference ) and real-time multimedia (e.g. real-time audio and video) support with a certain Quality of Service (QoS) level to mobile users. It is required that the mobile nodes have the capability of selecting services that are offered by each provider and determining the best path through the various networks. Efficient radio resource management (RRM) is one of the key issues required to support global roaming of the mobile users among different network architectures of the NGWS and a precise call admission control (CAC) scheme satisfies the requirements of high network utilization, cost reduction, minimum handover latency and high-level QoS of all the connections. This thesis is going to describe an adaptive class-based CAC algorithm, which is expected to prioritize the arriving channel resource requests, based on user¿s classification and channel allocation policy. The proposed CAC algorithm couples with Fuzzy Logic (FL) and Pre-emptive Resume (PR) theories to manage and improve the performance of the integrated wireless network system. The novel algorithm is assessed using a mathematical analytic method to measure the performance by evaluating the handover dropping probability and the system utilization.

In this thesis, an inverse source (IS) approach is considered for the constrained design of static-passive electromagnetic skins (SP-EMSs). By leveraging the ill-posedness/non-uniqueness of the IS problem at hand, a generalized solution framework is devised for the synthesis of SP-EMSs that simultaneously comply with (i) complex wireless coverage requirements and (ii) manufacturing and installation constraints. These two design goals can be decoupled and tackled separately through the employment of non-radiating (NR) currents. The flexibility of the IS-based formulation is demonstrated in practice with the implementation of two synthesis strategies dealing with different classes of design constraints. Representative results from a wide set of numerical experiments are reported to prove the effectiveness and the computational efficiency of the proposed method as a suitable tool for a real and effective realization of the so-called smart electromagnetic environment (SEME).

Microfluidics is a versatile tool with many applications in biology. Its ability to manipulate small volumes of liquid precisely has led to the development of many microfluidic assay platforms. They could handle small amounts of samples and carry out analysis with high sensitivity and throughput. Microfluidic assays have provided new insights into scarce biological samples at higher resolution. In this thesis, we developed microfluidic tools to conduct low input ChIP-seq and ChIRP-seq. We applied them to a variety of samples profiling different targets. The native MOWChIP-seq platform was developed to map RNA polymerase II, transcription factors and histone deacetylase binding in 1,000-50,000 cells. We examined mouse prefrontal cortex and cerebellum using this technology. We found extensive differences that correlated with distinct neurological functions of the brain regions. The same platform and workflow were used to profile five key histone modifications in human lung tumor and normal tissue samples. Integrative analysis with gene expression data revealed extensive chromatin remodeling in lung tumor. Spatial histone modification mapping was conducted in mouse neocortex in a similar fashion. We generated an epigenomic tomography that demonstrated the molecular state of the brain in 3D. Lastly, we developed a microfluidic version of the ChIRP-seq process which successfully conducted the assay using only 500K cells. This improvement makes ChIRP-seq in tissue samples feasible. / Doctor of Philosophy / Microfluidics is a type of technology that can control small volumes of liquid in a miniature system. It can carry out reactions on very small scales with higher precision and sensitivity than conventional methods. Microfluidics has found many uses in the field of biology, especially dealing with samples available in limited quantities. These low input microfluidic platforms have helped researchers gain new knowledge on many complex questions. In this thesis, we developed microfluidic tools to carry out low input ChIP-seq and ChIRP-seq. These are two established techniques used to map where certain targets are located on the genome of an organism. These targets include specific chemical modifications to the wrapper protein of DNA (histone modification), proteins that take part in transcription and expression of genes (RNA polymerase II, transcription factors) and other molecules. Our nMOWChIP-seq system removed the need for fixation by chemicals. It was able to examine RNA polymerase II, transcription factors and other enzymes using 1,000-50,000 cells. Traditional ChIP-seq requires more than 10 million cells and time-consuming chemical treatment steps. Our technology greatly improved sensitivity and ease of use. We also used this platform to test five important histone modifications in human lung tumors and healthy tissues. We constructed a spatial map of histone modification in mouse brain by analyzing slices of the cortex. Finally, we developed a microfluidic version of ChIRP-seq process to map locations of long non-coding RNAs in cultured human cells. The cells needed for a successful test were reduced to 500K from 20 million of the original workflow.

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Surface energetics play a key role in the delivery of a dry powder inhaler formulation into the lungs, as there must be a sufficient balance of adhesive and cohesive forces to allow optimal lung delivery. In this study, measuring the surface energies of a set of single drug and carrier (budesonide or theophylline with either mannitol or lactose) with different levels of surfactant using Inverse Gas Chromatography, and comparing them to their lung deposition performance using a Next Generation Impactor established a relationship between the two. A 1:10 mixing ratio of budesonide with either carrier was found to have the highest FPF. Coating the carriers with 0.05% sodium lauryl sulphate resulted in a further increase in the FPF when using either budesonide or theophylline as the API, and the same results were seen when a sonocrystallised version of the API was substituted for the micronised form. The calculated IGC values then showed that the highest performing formulations had the lowest dispersive energy and total free surface energy. Furthermore, a trend was observed in the work of adhesion (Wa) and work of cohesion (Wc) for each set of formulations depending on which API was chosen, where for the less polar drug (budesonide) a higher Wa/Wc ratio was associated with the highest formulation performance, and for the more polar drug (theophylline) a smaller Wa/Wc ratio was associated with the highest formulation performance, enabling the estimation of lung performance for a set of single drug and carrier using their surface energy data. / Kuwait’s government and the Ministry of Health of Kuwait

Antibiotic resistance is a global threat that can severely imperil public health. To curb the spread of antibiotic resistance, it is imperative that efforts commensurate with a “One Health” approach are undertaken. Given that interconnectivities among ecosystems can serve as conduits for the proliferation and dissemination of antibiotic resistance, it is increasingly being recognized that a robust global environmental surveillance framework is required to promote One Health. The ideal aim would be to develop approaches that inform global distribution of antibiotic resistance, help prioritize monitoring targets, present robust data analysis frameworks to profile resistance, and ultimately help build strategies to curb the dissemination of antibiotic resistance. The work described in this dissertation was aimed at evaluating and developing different data analysis paradigms and their applications in investigating and characterizing antibiotic resistance across different resistomes. The applications presented in Chapter 2 illustrate challenges associated with various environmental data types (especially metagenomics data) and present a path to advance incorporation of data analytics approaches in Environmental Science and Engineering research and applications. Chapter 3 presents a novel approach, ExtrARG, that identifies discriminatory ARGs among resistomes based on factors of interest. The results in Chapter 4 provide insight into the global distribution of ARGs across human fecal and sewage resistomes across different socioeconomics. Chapter 5 demonstrates a data analysis paradigm using machine learning algorithms that helps bridge the gap between information obtained via culturing and metagenomic sequencing. Lastly, the results of Chapter 6 illustrates the contribution of phages to antibiotic resistance. Overall, the findings provide guidance and approaches for profiling antibiotic resistance using metagenomics and machine learning. The results reported further expand the knowledge on the distribution of antibiotic resistance across different resistomes. / Antibiotic resistance is a global threat that can severely imperil public health. To curb the spread of antibiotic resistance, it is imperative that efforts commensurate with a "One Health" approach are undertaken. Given that interconnectivities among ecosystems can serve as conduits for the proliferation and dissemination of antibiotic resistance, it is increasingly being recognized that a robust global environmental surveillance framework is required to promote One Health. The ideal aim would be to develop approaches that inform global distribution of antibiotic resistance, help prioritize monitoring targets, present robust data analysis frameworks to profile resistance, and ultimately help build strategies to curb the dissemination of antibiotic resistance. The work described in this dissertation was aimed at evaluating and developing different data analysis paradigms and their applications in investigating and characterizing antibiotic resistance across different resistomes. The applications presented in Chapter 2 illustrate challenges associated with various environmental data types (especially metagenomics data) and present a path to advance incorporation of data analytics approaches in Environmental Science and Engineering research and applications. Chapter 3 presents a novel approach, ExtrARG, that identifies discriminatory ARGs among resistomes based on factors of interest. The results in Chapter 4 provide insight into the global distribution of ARGs across human fecal and sewage resistomes across different socioeconomics. Chapter 5 demonstrates a data analysis paradigm using machine learning algorithms that helps bridge the gap between information obtained via culturing and metagenomic sequencing. Lastly, the results of Chapter 6 illustrates the contribution of phages to antibiotic resistance. Overall, the findings provide guidance and approaches for profiling antibiotic resistance using metagenomics and machine learning. The results reported further expand the knowledge on the distribution of antibiotic resistance across different resistomes. / Doctor of Philosophy / Antibiotic resistance is ability of bacteria to withstand an antibiotic to which they were once sensitive. Antibiotic resistance is a global threat that can pose a serious threat to public health. In order to curb the spread of antibiotic resistance, it is imperative that efforts commensurate with the "One Health" approach. Since ecosystem networks can act as channels for the spread and spread of antibiotic resistance, there is growing recognition that a robust global environmental monitoring framework is required to promote a true one-health approach. The ideal goal would be to develop approaches that can inform the global spread of antibiotic resistance, help prioritize monitoring objectives and present robust data analysis frameworks for resistance profiling, and ultimately help develop strategies to contain the spread of antibiotic resistance. The objective of the work described in this thesis was to evaluate and develop different data analysis paradigms and their applications in the study and characterization of antibiotic resistance in different resistomes. The applications presented in Chapter 2 illustrate challenges associated with various environmental data types (especially metagenomics data) and present a path to advance incorporation of data analytics approaches in Environmental Science and Engineering research and applications. The Chapter 3 presents a novel approach, ExtrARG, that identifies discriminatory ARGs among resistomes based on factors of interest. The chapter 5 demonstrates a data analysis paradigm using machine learning algorithms that helps bridge the gap between information obtained via culturing and metagenomic sequencing. The results of Chapters 4 provide insight into the global distribution of ARGs across human fecal and sewage resistomes across different socioeconomics. Lastly, the results of Chapter 6 illustrates the contribution of phages to antibiotic resistance. Overall, the findings provide guidance and approaches for profiling antibiotic resistance using metagenomics and machine learning. The results reported further expand the knowledge on the distribution of antibiotic resistance across different resistomes.

Water reuse has become a promising alternative to alleviate stress on conventional freshwater resources in the face of population growth, sea level rise, source water depletion, eutrophication of water bodies, and climate change. Potable water reuse intentionally looks to purify wastewater effluent to drinking water quality or better through the development and implementation of advanced treatment trains. While membrane-based treatment has become a widely-adopted treatment step to meet this purpose, there is growing interest in implementing treatment trains that harness microorganisms as a more sustainable and less energy-intensive means of removing contaminants of emerging concern (CECs), through biological degradation or transformation. In this dissertation, various aspects of the operation of a microbially-active carbon-based advanced treatment train producing water intended for potable reuse are examined, including fate of dissolved organic carbon, underlying microbial populations, and functional genes are explored. Further, dynamics associated with antibiotic resistance genes (ARGs), identified as a microbially-relevant CECs, are also assessed. Overall, this dissertation advances understanding associated with the interplay between and within treatment processes as they relate to removal of various organic carbon fractions, microbially community dynamics, functional genes, and ARGs. Further, when relevant, these insights are contextualized to operational conditions, process upsets, water quality parameters, and other intended water uses within the water industry with the goal of broadening the application of advanced molecular tools beyond the scope of academic research. Specifically, this dissertation illuminates relationships among organic carbon fractions and molecular markers within an advanced treatment train employing flocculation, coagulation, and sedimentation (FlocSed), ozonation, biologically active carbon (BAC) filtration, granular active carbon (GAC) contacting, and UV disinfection. Biodegradable dissolved organic carbon (BDOC) analysis was adapted specifically as an assay relevant to assessing dissolved organic carbon biodegradability by BAC/GAC-biofilms and applied to profile biodegradable/non-biodegradable organic carbon as wastewater effluent passed through each of these treatment stages. Of particular interest was the role of ozonation in producing bioavailable organic carbon that can be effectively removed by BAC filtration. In addition to understanding the removal of fractionalized organic carbon, next generation DNA sequencing technologies (NGS) were utilized to better understand the microbial dynamics characteristic of complex microbial communities during disinfection and biological treatment. Specifically, this analysis was focused on succession and colonization of taxa, genes related to a wide range of functional interests (e.g. metabolic processes, horizontal gene transfer, DNA repair, and nitrogen cycling), and microbial CECs. Finally, NGS technologies were employed to assess the differences between a wide range of water use categories, including conventional drinking water, potable reuse, and non-potable reuse effluent's microbiomes to identify core and discriminatory taxa associated with intended water usage. The outcomes of this dissertation provide valuable information for optimizing carbon-based treatment trains as an alternative to membrane-based treatment for sustainable water reuse and also advance the application of NGS as a diagnostic tool for assessing the efficacy of various water treatment technologies for achieving treatment goals. / Doctor of Philosophy / Several factors have led to increased stress on conventional drinking water sources and widespread global water scarcity. Projections indicate that continued population growth, increased water demand, and degradation of current freshwater resources will negatively contribute to water needs and underscore the need to secure new potable (i.e. fit for human consumption) sources. Water reuse is a promising alternative to offset the growing demands on traditional potable sources and ameliorate negative consequences associated with water scarcity. Discharge of treated wastewater to marine environments is especially a lost opportunity, as the water will no longer be of value to freshwater habitats or as a drinking water source. Water reuse challenges the conventional wastewater treatment paradigm by providing advanced treatment of wastewater effluent to produce a valuable resource that can be safely used directly for either non-potable (e.g., irrigation, firefighting) or potable (i.e., drinking water) applications. The means of achieving advanced treatment of wastewater effluents can take many forms, commonly relying on the utilization of membrane filtration. However, membrane filtration is an intensive process and suffers from high initial costs, high operational costs, membrane fouling with time, and the production of a salty and difficult to dispose of waste stream. These drawbacks have motivated the water reuse industry to explore more sustainable approaches to achieving high quality effluents. One such alternative relies on the utilization of microorganisms to provide biological degradation and transformation of contaminants through a process known as biologically active filtration (BAF). Comparatively to membrane systems, BAF is more cost effective and produces significantly fewer byproducts while still producing high quality treated water for reuse. However, the range in quality of the resulting treated water has not yet been fully established, in part due to the lack of understanding of the complex microbial communities responsible for biological treatment. As water and wastewater treatment technologies have evolved over the past century, many biological treatments have remained largely 'black box' due to the lack of effective tools to identify the tens of thousands of species of microbes that inhabit a typical system and to track their dynamics with time. Instead, analysis has largely focused on basic water quality indicators. This dissertation takes important steps in advancing the implementation of the study of DNA and biodegradable organic carbon (BDOC) analysis to improve understanding of the mechanisms that drive different water reuse treatment technologies and to identify potential vulnerabilities. Insights gained through application of these tools are contextualized to observed operational conditions, process upsets, and water quality measurements. This helped to advance the use of DNA-based tools to better inform water treatment engineering practice. Specifically, this dissertation dives into the relationships between organic carbon and DNA-based markers within an advanced treatment train employing flocculation, coagulation, and sedimentation (FlocSed), ozonation, biologically active carbon (BAC) filtration, granular active carbon (GAC) contacting, and UV disinfection. Development and application of the BDOC test revealed that the bulk of organic carbon entering the treatment train is dissolved. Further, BDOC analysis served to characterize the impact of specific treatment processes and changes in operational conditions on both biodegradable and non-biodegradable organic carbon fractions. Such information can help to inform continued process optimization. Utilization of DNA-based technologies shed light on the functional capacity of microbial communities present within each stage of treatment and the fate of antibiotic resistance genes (ARGs). ARGs are of concern because, when present in human pathogens, they can result in the failure of antibiotics to cure deadly infections. Other functional genes of interest were also examined using the DNA-based analysis, including genes driving metabolic processes and nitrogen cycling that are critical to water purification during BAF treatment. Also, the DNA-based analyses made it possible to better understand the effects of disinfectants on microbes. Interestingly, some ARG types increased in relative abundance (a measure analogous to percent composition) response to treatments, such as disinfection, and others decreased. Characterization of the microbial communities and their dynamic response to changing operation conditions were also observed. For example, it was possible to characterize how the profiles of microbes changed with time, an ecological process called succession, during BAC filtration and GAC contacting. Generally, this analysis, coupled with the functional analysis, shed light on the important, divergent roles of bacterial communities on organic degradation during both BAC and GAC treatment. Finally, a study was conducted that compared the microbiome (i.e. entire microbial community) between a wide range of conventional drinking water, potable reuse water, and non-potable reuse waters. Here it was found that significant differences existed between the microbial communities of water intended for potable or non-potable usage. This work also looked to expand the application of NGS technologies beyond strictly academic research by developing the application of more advanced DNA-based tools for treatment train assessment and monitoring.